Spaceborne Interferometry for Bridge Monitoring: Advancing Structural Integrity Management Through Remote Sensing
Bridges are essential components of civil infrastructure, and ensuring their structural integrity is crucial for public safety and service continuity. Recent advancements in Synthetic Aperture Radar Interferometry (InSAR) have significantly enhanced our ability to remotely monitor structural behavior, offering a valuable complement to traditional inspection and sensor-based methods. The recently published Italian guidelines for InSAR monitoring of civil structures, developed under a project funded by the Italian Civil Protection, provide essential standards and best practices for integrating InSAR into infrastructure management. The European Ground Motion Service, part of the Copernicus program, provides valuable ground motion data across Europe using Sentinel-1 imagery. In addition to routine monitoring, InSAR holds substantial promise for post-event forensic analysis. It can help engineers investigate the underlying causes of bridge failures by revealing patterns of progressive displacement leading to collapse, critical information that traditional inspection and monitoring methods often miss. This keynote explores the use of spaceborne InSAR for structural health monitoring of bridges, focusing on InSAR data clustering for the interpretation of bridge behaviour, and collapse investigation.
100th Anniversary of the Monitoring of Stevenson Creek Experimental Dam: Reflections on the Past and Future Advancements on Strain Sensing and Strain-Based Monitoring of Civil Structure
Stevenson Creek Experimental Dam was bult near Fresno, California, in 1924-1925, with the aim of studying the structural behavior of arch concrete dams. To achieve the project objectives, six types of sensors were deployed: four types of sensors were based on mechanical devices that were operated manually, but two novel types of sensors, deflection (relative displacement) sensors and embeddable strain sensors (consisting of a stack of carbon discs), were both based on electrical resistance and were assessed remotely, via wires. While the use of mechanical manual sensor technologies in real-life applications can be traced back to the 19th century, what makes the application of novel sensors in Stevenson Creek Experimental Dam paradigm changing, is the fact that the readings of resistive deflection (relative displacement) and strain sensors were performed from a remote room. The centenary of this technological achievement, which represents the first application of modern monitoring technique in the United States of America (to the author’s best knowledge), stimulates reflection on the past accomplishments and the future advancements on strain sensing and strain-based monitoring of civil structures. Hence, the aim of this paper is to summarize the progress in strain sensing technologies and their impact on strain-based monitoring over the first hundred years, and to give a few glimpses about directions of future developments.
Enhanced SHM Using Tensor Decompositions and Sparse Atomistic Models for Damage Monitoring of Aeronautical Composite Structures
Developing robust Structural Health Monitoring (SHM) solutions for large structures, particularly in the aerospace sector, remains challenging due to the volume, variability, and complexity of the data involved. One very promising solution is based on active ultrasonic guided waves (UGW) which are signals emitted and received by a set of transducers bonded to the structure to monitor. However, existing SHM algorithms cannot solve the aforementioned challenges under the current paradigm of path-by-path processing of the raw UGW signals. To move forward, a new paradigm is introduced in this work. This new approach exploits the intrinsic multi-dimensional tensorial nature of SHM UGW data through Canonical Polyadic Decomposition (CPD) and couples it with the Single Atom Convolutional Matching Pursuit Method (SACMPM). This redefines classical sparse decomposition techniques building accurate and efficient wave propagation models tailored to SHM applications. A unique UGW database where regular ground-based measurements have been carried out on an actual A380 running flight test is described in order to challenge the proposed paradigm shift. The efficiency of the coupling between SACMPM and CPD is illustrated here with respect to their ability to compress UGW information, and extract meaningful information from UGW signals in a physically informed manner. Additionally, a CPD-based damage localization algorithm is enriched using a SACMPM decomposition. Extracted physically informed features can thus be efficiently used for physically informed data driven damage monitoring approaches. The proposed paradigm shift thus demonstrates a strong potential for scalable, transferable, and reliable UGW SHM solutions, bridging the gap between laboratory experiments and real-world deployment. This paradigm shift is also expected to inspire further research and innovative ideas, leading to breakthroughs in the adoption of active UGW signals for SHM applications.
Triple Functional UPI (Ultrasonic Wave Propagation Imager) and Applications to Aircraft
As a fast nondestructive inspection (NDI) system, triple functional ultrasonic wave propagation imager (UPI) was developed. It features three modes: Q-switched laser scan guided UPI (QL-scan G-UPI), angular pulse-echo UPI (A PE UPI), and laser Doppler vibrometer scan guided UPI (LDV-scan G-UPI). QL-scan G-UPI utilizes Lamb waves created by the Q-switched laser, with a PZT sensor receiving waves distorted by structural damage. A PE UPI employs bulk waves induced by the same laser, and the LDV captures bulk waves reflected from the back face or internal defects in the thickness direction. LDV-scan G-UPI uses standing waves generated by a PZT exciter, which are captured by the same LDV used in the A PE UPI. By employing acoustic wavenumber spectroscopy (AWS), the thickness changes in structures due to damage can be identified through the calculated wavenumber of the standing waves. A significant advantage of this equipment is its inspection speed. The maximum scanning speeds of each mode are 32.11 m2/h for A PE UPI, 23.21 m2/h for QL-scan G-UPI, and 28.35 m2/h for LDV-scan G-UPI, respectively. The scanning speed can be adjusted based on the required precision of the damage detection, as smaller scan intervals are required to detect minute internal damage. The post processing algorithms for diagnosing structures in this equipment are variable time window amplitude mapping (VTWAM), ultrasonic energy mapping (UEM) and AWS. It takes less than one minute to visualize the results for a 1 x 1 m2 plate. The current equipment was evaluated by detecting various types of damage in aircraft structures. The detected damage includes cracks and corrosions in aluminum structures, as well as film inserts, debonding and delamination in composite structures. In particular, damage growth in a composite plate during fatigue tests, and debonding between rivets in an aircraft were successfully detected using A PE UPI without any disassembly or interference.
Applicability of Loads Estimation Techniques Using Sparse Acceleration Sensor Data to Spacecraft Structural Health Monitoring
The use of structural health monitoring systems on spacecraft structures can play a crucial role in ensuring the safety, reliability, and longevity of the structure by gathering and analyzing onboard sensor data. Of specific importance is monitoring for excessive loading at critical interfaces as any unexpected structural excitations experienced by spacecraft structures can cause early unpredicted high structural life consumption and/or damage. The availability and cost of flight-certified sensors along with the size of spacecraft structures and allowable payload mass drives the need for a method to estimate loads using sparsely-located sensors. Numerous approaches such as physicsbased, statistical learning, and physics-enhanced statistical learning algorithms have gained popularity among structural prognostics applications. However, developing noise-robust prediction models to assess loads and structural life predictions from a sparse multi-sensor data acquisition system can be a challenging task. This paper discusses the evaluation of physics-based versus machine learning algorithms for predicting loads and structural life at mission-critical locations on the spacecraft structure using a finite element loads analysis with the application of simulated noise and noise reduction techniques. To estimate the loads from accelerations, the physicsbased algorithm leverages a loads transformation matrix (LTM) from a Hurty-Craig- Bampton (HCB) reduced finite element model. A System Equivalent Reduction Expansion Process (SEREP) and a pseudo-inverse approach are considered to expand from the onboard sensor degrees of freedom to the HCB model degrees of freedom. The machine learning algorithm provides a data-driven solution/mapping of the sensor accelerations to the loads at the mission-critical locations using a high dimensionality analysis. Although these strategies produce comparable loads prediction without noise, the limitations of these strategies with incorporating simulated noise and noise reduction techniques with low signal to noise ratio signals are evaluated. The study demonstrates the immense potential of statistical learning algorithms for sparse structural prognostic models and enhancing signal denoising techniques. These findings also highlight the need for noise-resilient prognostic models and low-noise data acquisition systems onboard spacecraft structures.
Digital Twinning of Aerospace Structures
Digital twinning has emerged as a cutting-edge technology with significant applications across various industries, including the aerospace sector. Aerospace components operate in harsh environments that may induce severe loads, increasing the risk of catastrophic failures and resulting in substantial human and financial implications. To mitigate these risks, a digital twin framework can be employed for structural health monitoring. Although multiple digital twinning methodologies exist, each with distinct advantages, the inverse Finite Element Method (iFEM) stands out due to its capacity for transforming discrete strain sensor readings into full-field structural response data. This comprehensive dataset—which can encompass displacements, strains, and stresses—enables the construction of an accurate digital representation of the structure. By applying relevant failure criteria to these data, potential damage can be identified at early stages, allowing for timely maintenance or corrective actions. In this study, representative aerospace structures composed of both isotropic and composite materials will be investigated. Collected strain sensor data will be processed via the iFEM methodology to generate digital twins of these structures. Through this approach, the applicability and effectiveness of iFEM for aerospace applications will be demonstrated, highlighting its potential to enhance safety and reduce operational costs in the industry.
Real-Time Structural Monitoring Using the Inverse Finite Element Method: A Review of Experimental Applications in Aerospace Engineering
The realization of an efficient Structural Health Monitoring (SHM) system strongly depends on the availability of tools that can accurately detect the structural condition during operational life. This detection can be achieved by means of physical sensors, which can provide information on the mechanical state of a structure. However, the installation of several sensors is often impractical or even impossible for systems operating in complex environments, such as aerospace, marine, and civil structures. In this context, the inverse Finite Element Method (iFEM) was developed. This algorithm is based on the finite element discretization of the structural domain. iFEM is able to reconstruct two physical quantities crucial for assessing the health status of a structure, the displacement and the stress fields, from a reduced number of easily installable physical strain sensors and without the need to know the loading conditions and the material properties. The method is extremely accurate and computationally efficient. These characteristics make it suitable for the real-time monitoring of structures and crucial for feeding the SHM framework with sufficient data using only a few sensors installed. Several works have proved the method’s accuracy when strain data are collected and processed offline. For the first time, this work presents an overview of the implementation of structural digital twins of several structures through the use of iFEM as a real-time monitoring system. In this study, both simple structures, such as a cantilevered beam, and more complex ones, such as a half-wing, are monitored live. In the presented applications, the data from the real structure is broadcast through an internet network so that the data from the sensors can feed the digital twin model remotely. These applications demonstrate that iFEM enables real-time monitoring, representing a significant step forward in the realization of the digital twin paradigm.
Shape Reconstruction of Post-Buckled Panels in a Wagner-Beam Test-Rig Using Distributed Optical Sensors and Digital Image Correlation
This paper compares out-of-plane displacement measurement techniques between a digital image correlation system and a distributed optical fiber sensor system for shearloaded panels in a Wagner beam. The digital image correlation system determines the displacement directly and the distributed optical fiber sensors use Ko’s displacement theory for strain-based displacement calculation. In this study, the Wagner beam was shear-loaded and the panel strain was elastic and reversible. This paper shows that the digital image correlation system is state-of-the-art for determining the panel’s out-ofplane displacement. The displacement estimation by the distributed optical fiber system requires a precise setup and an extension of Ko’s displacement theory for this specific testing situation.
Structural Health Monitoring of UAV Wing Platform Using FBG Sensors, Wavelet Transform, and Computational Intelligence
The growing demand and interest in the use of Unmanned Aerial Vehicles (UAVs) for various applications has highlighted the need to develop more robust structural monitoring systems, particularly for aircraft constructed with composite materials. Although these materials offer advantages in terms of lightness and strength, they are susceptible to delaminations and microcracks, which can compromise the safety and operational efficiency of UAVs. Early detection and characterization of these defects are key to preventive maintenance strategies and structural design optimization. This study presents a structural monitoring architecture based on Fiber Bragg Grating (FBG) sensors, combined with signal processing methods and computational intelligence, to evaluate the structural integrity of UAV wings. Luna Innovations’ os1200 and os3200 sensors were selected for their high sensitivity, immunity to electromagnetic interference, and multiplexing capability on a single fiber. The os1200 sensors are positioned in critical areas of the wing profile, allowing for the mapping of stress distribution in high-tension regions, while the os3200 sensors are placed in hard-to-reach areas where the use of metallic sensors is not feasible. This arrangement facilitates detailed data acquisition on the strain distribution across the UAV structure. For data analysis, a methodology based on signal processing and machine learning was employed. Filtering and conditioning techniques were applied to reduce noise, followed by Fourier and wavelet transforms, which enabled the identification of subtle changes in the structural response typically associated with the presence of faults. Additionally, artificial neural networks and machine learning algorithms were implemented for defect classification and severity assessment, leveraging patterns extracted from the sensor signals. Hybrid models combining wavelet transforms with supervised learning were explored, optimizing the detection and prediction of structural damage. The initial validation of the architecture was conducted in a controlled laboratory environment, using UAV wing profile prototypes subjected to static and dynamic load tests to induce different types of failures. FBG sensor measurements were correlated with visual inspections and non-destructive evaluation (NDE) techniques. The results obtained are expected to lay the groundwork for the development of a real-time structural monitoring system that enhances the safety and reliability of UAVs, reduces the risk of catastrophic failures, and provides a key tool for intelligent maintenance management.
Field Operation of PZT Sensor Based SHM System on Commercial Aircraft in Detecting Cracking Around Chem-Mill Areas
The integrity of Boeing 737 aircraft structures, particularly at the edges of chemically milled pockets between stringers and frames on the interior skin surface, is compromised by the occurrence of multiple crack formations. These cracks, often initiated at the chem-mill line, have prompted regulatory agencies and manufacturers to enforce rigorous inspection protocols, including Eddy Current and Phased Array Ultrasonic Testing (PAUT). While effective, these NDT methods are labor-intensive, costly, and often necessitate extensive aircraft disassembly, posing safety risks to inspectors, creating logistical challenges, and incurring significant operational downtime. The sheer volume of inspection sites, coupled with the limited availability of qualified personnel and the difficulty of accessing critical areas, underscores the need for more efficient, reliable, and less invasive inspection techniques. To address these challenges, Delta Air Lines has shown interest in investigating Structural Health Monitoring (SHM) as a cost-effective solution that could eliminate the need for extensive access requirements. This study presents an evaluation of SHM technology's feasibility in detecting cracks along the chem-mill line on Boeing 737 structures in service condition.
An Optimized iFEM for High Precision Real-Time Deformation Sensing of Composite Antenna Panel
In recent years, composite structures have gained extensive applications in the aerospace industry owing to their high strength-to-weight ratio, lightweight nature, and superior corrosion resistance. Notable examples include large phased-array antenna panels, composite wing skins, wing box sections, fuel tanks, and rocket fairings, etc. Taking the large phased-array antennas as a representative case, their antenna panels are constructed from multi-layered heterogeneous honeycomb sandwich panel, and serve as critical components for hosting functional elements of satellite antennas, which play a pivotal role in enabling core satellite functionalities. However, during on-orbit operations, these panels are exposed to the harsh and complex space environment, inevitably leading to unpredictable structural deformations that jeopardize satellite performance and operational safety. Consequently, research on deformation sensing for composite antenna panels holds significant engineering urgency. Among various deformation sensing methodologies, the inverse Finite Element Method (iFEM) has emerged as a highly promising technology due to its unique advantages: independence from structural parameters (e.g., mass and stiffness), no reliance on prior information, and capability for three-dimensional deformation reconstruction. The core principle of iFEM involves discretizing the structure into a series of inverse elements, characterizing theoretical strains via nodal displacements, and solving for these displacements by minimizing an error function between theoretical and measured strains, thereby enabling deformation sensing. Nevertheless, in practical aerospace applications, structures often operate under complex loading conditions and boundary constraints. Traditional iFEM approaches face challenges in achieving high-precision deformation reconstruction while maintaining real-time performance, limiting their applicability in mission-critical scenarios. To address these limitations, this study introduces a fundamental optimization of the iFEM framework, which significantly improves the accuracy and real-time performance of deformation sensing of composite antenna panels. Simulation and experimental results show that the proposed iFEM method can further improve the deformation reconstruction accuracy while ensuring real-time performance, and provide a reliable technical support for structural health monitoring under complex working conditions.
Effect of Aero-Engine Washing on Turbine Gas Temperature Margin
This study investigates the effects of engine washing on T700 engine performance and material deterioration. To assess performance improvement, turbine gas temperature margin data from six Sikorsky S-70 aircraft were analyzed using linear mixed model. The statistical test yielded a p-value below 0.05, rejecting the null hypothesis that there is no significant difference in the mean turbine gas temperature margin before and after engine washing. Regarding material impact, alternate immersion and potentiodynamic test indicated that residual chloride and sulfate ions in the washing water can increase corrosion risk. Additionally, exposure experiments confirmed that the wetting of metals post-washing could also trigger corrosion. Considering these conflicting effects, a Bayesian decision model was applied to determine the economic feasibility of engine washing strategies. The results indicate that engine washing is economically advantageous. Furthermore, considering both risk cost and washing cost, the analysis suggests that an optimal washing interval is 190 hours. The results of this study are expected to contribute to establishing an optimal engine corrosion management plan.
High-Fidelity Analytical Approaches for Damage Detection and Monitoring in Curved Composite Beams
Structural analysis and structural health monitoring (SHM) are essential procedures for ensuring the safety and operational efficiency of aerostructures, particularly composite laminates with complex geometries subjected to static or dynamic loadings. These structures, widely used in aerospace applications such as aircraft nacelles, are often susceptible to impact induced damages which may compromise the performance of structural parts leading to undesirable outcomes. Consequently, characterising the effect of damage on the performance of composite aerostructures becomes inevitable. To mitigate the time-demanding in-service assessment of aerostructure components, this research proposes a high-fidelity analytical-based SHM model for damage detection in curved composite beams. Firstly, a one-dimensional (1D) analytical model which analyses the mechanical response of curved composite beams under arbitrary boundary and loading conditions is developed. The 1D model adopts the kinematical descriptions of refined Timoshenko beam theory enhanced with deterministic continuum damage mechanics principles to functionally achieve stiffness degradation that typifies bulk damages. The ensuing equilibrium equations are then analytically discretised leading to a global system with which the natural and resonant frequencies of damaged structures are simulated. Calibration of the 1D model’s capacity through comparison with 3D Finite Element (FE) solutions and literature benchmarks indicate high-fidelity and significant computational savings in degrees of freedom and runtime. The 1D model is subsequently integrated with swarm intelligence-based optimization algorithms to detect damage in composite laminates. A demonstration of the capability of the analytical-based SHM approaches reveals that rapid damage detection and quantification across various material configurations and boundary conditions can be achieved with satisfactory accuracy emphasising the robustness of the SHM scheme. In view of this outcome, the proposed SHM model offers great potential for real-time SHM applications while contributing to reduced maintenance costs and enhanced structural reliability of composite aerostructures. Keywords: SHM, 1D analytical model, damage detection, swarm intelligence-based optimization
History and Challenges for Structural Health Monitoring (SHM) for Fixed Wing Military Aircraft
The concept of using permanently attached sensors to detect damage in fixed wing aircraft structure has been explored since the late 1970s. The types of sensing that has been explored include various acoustic, eddy current, mechanical, and strain-based sensing systems. However, to date none of these systems have been installed on Department of the Air Force (DAF) fixed wing aircraft except for experimental applications. This paper reviews past efforts within the DAF to investigate using integrated sensors to replace current nondestructive inspection (NDI) processes. The discussion of past projects cannot be comprehensive due to the large volume of literature in this field, but will cover each primary sensing modality, typical sensor types, and signal processing approaches. In addition, how the sensing techniques integrate with the DAF Aircraft and Structural Integrity (ASIP) Military Standards, such as MIL STD 1530D, is a critical attribute for sensing systems to become direct replacements for current NDI processes. The needs of fixed wing aircraft are compared to the successful implementation of sensors on helicopters to detect evolving change in rotating drivetrains.
Multi Parameter SHM System for Damage Detection in Real Scale Composite Wing for Next Generation Green Regional Aircraft
This paper presents the validation of a multi-parameter and multi-fidelity structural health monitoring-methodology based on guided waves. An accurate design is carried out to define a comprehensive system capable of identify impact loads and characterize the consequent damage in complex aerostructures. The design is based on several technologies to offer multi-level diagnosis. In addition, different excitation and sensing techniques are applied using transducers deployed all over the structure. The conceived appoach is applied on a 9m long composite wing box for next generation regional aircraft, with the main aim of detecting barely visible impact damage via integrated sensors data. Both delamination and debonding of stiffened components are experienced via an extensive experimental campaign. The first results obtained in detecting and localization damage are discussed, showing the effectiveness of this approach.
Implementation of Piezoelectric (PZT) Sensors on the Boeing 737NG Aft Pressure Bulkhead Structure
This paper summarizes the results of the validation testing conducted to support the development of piezoelectric (PZT) sensors for crack detection in the 737NG Aft Pressure Bulkhead (APB) web at the Y-chord. The objectives of the tests were to evaluate detection capability, durability and reliability of PZT sensors under various environments and temperatures. The test results herein were collected from Boeing Laboratory, the FAA Technical Center and Stanford University. The results contained in this document are for engineering development purposes.
Testing for Certification of CVMTM Sensors on the 737NG Aft Pressure Bulkhead
Boeing has invested in the development of health monitoring sensors over the years and is currently working multiple projects to implement such technology as viable inspection methods. In 2016, Boeing added Comparative Vacuum Monitoring (CVMTM) sensors as an alternative inspection method on the 737NG Center Wing Box fittings utilizing an economic service bulletin. In 2018, Boeing partnered with Anodyne Electronics Manufacturing (AEM) Corp and Delta TechOps to pursue using CVM sensors on the web of the 737NG Aft Pressure Bulkhead as an alternative inspection to Service Bulletin (SB) 737-53A1248. This is a safety bulletin due to reported in-service cracks on the 737 classic models and is mandated by the FAA with an Advisory Directive (AD). In 2022, Boeing launched the certification project. This paper will cover some of the testing that went into the certification of CVM sensors for this targeted application. Boeing has received FAA approval for their method of compliance issue paper and certification plan. Boeing has completed all tests and demonstrated that the CVM sensors are as effective as low frequency eddy current (LFEC) inspections for this application. Service Bulletin (SB) 737-53-1418 is being developed to allow operators to use CVM for this application. Boeing is currently finalizing the certification documents for submission to the FAA. CVM sensors were installed at the critical location - the web to “Y” chord attachment between stringers 5 to 7 on the left side and stringers 5 to 9 on the right side. The proposed CVM option will demonstrate equivalency to LFEC and utilize the same inspection interval. The CVM system is expected to reduce the inspection time from 24 hours to 15 minutes by eliminating the access removals (aft galley, insulation blankets) or tailcone entry, needed to allow inspector admission to conduct the traditional nondestructive inspection.
Route to Condition Based Maintenance in the Commercial Aviation Industry—Developments in Operational Monitoring
In order to have more efficient maintenance, reduce down time and optimize fleet operation, Airframe Structural Health Monitoring targets progressive solutions with the ultimate objective to offer structural Condition Based Maintenance. Enablers will be SHM-sensors, operational monitoring and automation. Collaboration will be key between aircraft manufacturers, operators, maintenance and repair organisations, airworthiness authorities, scientific institutes and the standardization bodies. The development of operational monitoring will enable maintaining the airframes when needed, thanks to the knowledge of individual aircraft usage, data exploitation and digital twins. This paper predominantly puts the focus on the operational monitoring aspect of Structural Health Monitoring.
Shear Horizontal Waves Converted from Piezoelectric-Transducer-Activated Lamb Waves Using Topologically Designed Meta-Converters
Shear horizontal (SH) waves exhibit appealing features like non-dispersion (SH0 waves) and immunity to material nonlinearity, which benefit a range of applications, such as structural health monitoring (SHM). However, their effective excitation is challenging in practice due to the weak intensity of generated waves or the installation inconvenience of transducers. In this study, we propose an SH wave generation scheme based on mode conversion from piezoelectric-transduceractivated Lamb waves, with the help of add-on metamaterials. To this end, metaconverters, in conjunction with topology optimization methods, are employed to achieve high-efficiency conversion from Lamb waves to SH waves. 3D finite element models are employed to enable the destruction of structural symmetry in the out-of- plane, which is necessary for Lamb-wave-to-SH-wave conversion. In a broad band around the operation frequency, typical scenarios involving incident Lamb waves of varying modes and frequencies are considered, leading to a series of meta-converter designs. Taking incident waves as a baseline, the efficiency of the conversion is quantified in terms of the magnitude of the transmitted SH waves. Time-domain responses are analyzed to verify the efficient generation of SH waves. Finally, representative meta-converters are 3D-printed using the selective laser melting technique, with their capability for SH wave enhancement further validated by experiments. The proposed method offers an alternative and more generic route for SH wave generation, and paves the way for further SH-wave-based SHM applications.
Characterization of Lamb Wave Damage Interactions for Pseudo Damage Development
In aeronautics delamination damage in composite structures are a crucial factor for the timing of maintenance cycles. Structural health monitoring systems based on ultrasonic guided waves have the potential to find them quickly and consequently save airlines money. For the development of such a system removable pseudo damage are a cost-efficient alternative to enable the necessary quantity of testing. However, to develop a removable pseudo damage, a strategy is needed to classify the wave interactions of delaminations in a way which can also be used to classify those of removable pseudo damage. Therefore, this paper will showcase a methodology to classify the Guided waves reflection, transmission and S0 to A0 mode conversions induced by delaminations and discuss potential pseudo damage types. The analysis is based on a b-scan taken by an air coupled ultrasonic scanner starting at the actuator and ending behind the damage. The key findings are that the suggested methodology was able to capture the wave interactions of the delaminations as well as the experimental pseudo damage. Thus, it could be used as a tool to test the interactions of pseudo damage and compare it to the ones of delamination. Hence, being the first step towards enabling high-volume damage localisation tests of structural health monitoring systems.
Damage Detection of Miter Gates Through Nonlinear Model Updating
Miter gates are a critical component of many navigation locks in the inland waterway infrastructure. Due to deterioration and impacts from debris or vessels, gaps can develop between the bearing surfaces supporting the miter gates. This loss of bearing contact creates a redistribution of stress that is typically not accounted for in design, which can lead to accelerated deterioration and possibly unexpected failure of the gate. We present a novel approach to detecting the loss of bearing contact by finite element (FE) model updating. The contact interface is modeled using nonlinear springs with unknown parameters. These parameters are estimated by minimizing the difference between in-situ measured strain and the strain predicted by the FE model. This is naturally formulated as an optimization problem, where the unknown spring parameters are the optimization variables. The ensuing optimization problem is nonconvex, therefore, multiple local searches are performed to increase the chance of finding the global optimum. Two types of springs are considered for modeling the nonlinearity of the boundary conditions, namely, a gap element and a cubic spring. This paper presents both the formulation and validation of the proposed approach.
Mechanic-Electric-Thermal Coupling Simulation Method of Lamb Wave Under Variable Temperature
The propagation mechanism of Lamb waves exhibits more intricate in the variable operational environment of high-speed trains. Simulation methods are widely used to study Lamb wave propagation characteristics due to economic and time constraints. A mechanic-electric-thermal coupling simulation method to study the propagation mechanism of Lamb waves under variable temperatures is proposed in this paper. To accurately simulate the dynamic behavior of Piezoelectric Transducer (PZT) under variable temperature, a nonlinear numerical model of the dynamic parameters (piezoelectric coefficient, dielectric constant) of PZTs has been developed based on the experiment of the temperature influence on Lamb wave. The model emphasizes the nonlinear pattern of change in the key dynamic parameters of the PZT with temperature fluctuations. In addition, a thermal expansion stress model of the PZT-bonding layersstructure is established to reveal the mechanical-thermal coupling mechanism. Based on the COMSOL, a mechanical-electrical-thermal direct coupling simulation model of PZT-bonding layers- structure under variable temperature is established. The simulation results at - 40 ◦C to 80 ◦C are obtained and compared to the experiment. The results show that the simulation signal is highly consistent with the experimental measurement signal, and the simulation method proposed has high accuracy.
Active Generation and Propagation of Quantum Analogous Spin State of Guided Waves for Topological NDE/SHM
The spin topology of guided waves presents new opportunities for Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE). By leveraging spinmomentum locking, damage detection techniques can be enhanced through controlled wave propagation, enabling selective energy transport and improved sensitivity to structural anomalies. Traditional ultrasonic methods rely on wave reflections and mode conversions, whereas spin-mediated guided waves offer additional degrees of freedom to analyze wave interactions with defects. In this study, the intrinsic quantum analogous spin states of guided ultrasonic waves in elastic waveguides are examined from a quantum-inspired perspective. Unlike conventional interpretations of guided wave propagation, we demonstrate that spin angular momentum (SAM) emerges naturally from the superposition of elastic wave potentials. To apply these principles in SHM and NDE, we propose approaches utilizing conventional piezoelectric transducers and laserbased sensing techniques. In this study the utilization of spin state and its effect on topological geometric phase is demonstrated through virtual experiments. The ability to manipulate spin states in guided waves introduces a promising framework for advanced structural diagnostics, paving the way for more robust and efficient monitoring solutions in engineering applications.
Minimally Intrusive SHM Systems Using Piezoelectric Nanofibers in Composite Laminates
This work presents the development of a self-sensing Carbon Fiber Reinforced Polimer (CFRP) laminate embedding P(VDF-TrFE) nanofibers for real-time impact detection and localization. Four nanostructured sensors were embedded within the midplane of cross-ply laminates and compared against laminates incorporating commercial PZT sensors. A probabilistic impact localization algorithm based on Gaussian Process Regression and differential Time of Arrival (dToA) measurements were implemented. While PZT sensors exhibited higher localization accuracy (RMSE=2.32 cm) compared to nanofiber-based sensors (RMSE=3.37 cm), they caused more severe interfacial damage under low-velocity impacts. In contrast, the nanostructured sensors confined damage to the polymeric interlayer, preserving the CFRP integrity. The results highlight the potential of nanofiber-based sensing layers as a scalable, minimally invasive solution for Structural Health Monitoring in aerospace composites.
Actuation Model for Lamb Wave Excitation in Composite Plates Using Piezoelectric- Flexoelectric Actuators
Among various SHM techniques, guide-wave-based methods are mostly explored for their effectiveness in damage detection. However, despite the growing interest in this area, no analytical models have been developed to date for the actuation of Lamb waves in composite structures using piezoelectric and flexoelectric transducers. This article develops a theoretical model for actuation of composite plates using physically modeled adhesively bonded piezoelectric-flexoelectric transducers. The model incorporates both interfacial shear and peel stresses at the interface of the actuator and host composite laminate. The tractions induced by the actuator onto the plate surface are determined by modeling the actuator and the laminated plate as Mindlin plates. The formulation results in a seventh-order differential equation for the interfacial shear stress, which is solved in closed form by satisfying stress-free boundary conditions at the actuator and plate edges. The peel stress is subsequently determined from the differential relation in terms of the interfacial shear stress. For validation, the longitudinal distributions of interfacial shear and peel stresses are compared with the ABAQUS’ continuum-based finite element results. A comprehensive numerical study has been performed to demonstrate the effect of adhesive and host laminate thickness on the distributions of interfacial shear stress and its peak amplitude.
Smart Mechanoluminochromic Coating for Impact Damage Detection in Composite Structures
The susceptibility of composite materials to impact loads remains a critical challenge in aerospace applications. Conventional Structural Health Monitoring (SHM) systems employ embedded or adhesively bonded sensors, such as Bragg gratings or ceramic piezoelectrics, which compromise the mechanical integrity of the laminate and introduce complexities in signal acquisition due to optical, electrical, or wireless connections. Mechanoluminochromic (MLC) materials offer a transformative solution by exhibiting fluorescence emission spectrum changes under mechanical stimuli. This study presents a novel smart coating utilizing an MLC Cu-based Hybrid Coordination Polymer (HCP) dispersed into a thermoset epoxy-based paint. Applied via spray painting on a cross-ply carbon fiber reinforced plastic (CFRP) laminate, the coating enables full-field impact damage detection under UV illumination without altering the aesthetic appearance of the structure. Low-velocity impacts (LVI) were conducted at varying energy levels, and the resulting barely visible damage (BVD) was analyzed using smartphone-acquired images processed with Matlab algorithms. The green/blue fluorescence ratio demonstrated correlation with impact forces, while micrograph analysis of the laminate cross-section confirmed damage alignment with mechanoluminochromic responses. This innovative nondestructive inspection (NDI) technique offers cost-efficiency, reliability, and ease of implementation, establishing a promising avenue for SHM applications with potential real-time computer vision integration.
Wireless Embedded Sensing of Strain and Temperature
In this study, we demonstrate the first realization of wireless strain and temperature sensing within 3D-printed metallic structures using standard inspection hardware. This establishes a path toward need-based parts maintenance driven by accurate damage assessments instead of relying on regularly scheduled maintenance teardowns, extending the service intervals of structures operating in harsh environments. To this end, we encapsulate magnetoelastic and thermomagnetic materials inside microtubes and embed the sensing elements mid-print. Mechanical and thermal stimuli affect the magnetic permeability of the embedded materials, which modulates the induced voltage in a coil placed on or near the surface of the printed part. We demonstrate strain sensing accurate to ±25×10-6 over a 1×10-3 strain range, and temperature sensing accurate to ±0.75°C over a 70°C range, both to a 95% confidence interval. This extends non-destructive eddy-current damage detection to accurate, real- time strain and temperature monitoring in aluminum alloys and other metals.
Study on a Novel Dual-Layer Graded Honeycomb Metamaterial for Bridge Anti-Collision Structures
With the rapid expansion of bridge infrastructure and the increasing frequency of ship-bridge collision accidents, traditional anti-collision measures have exhibited limitations in dispersing and absorbing impact energy. This study proposes a novel duallayer composite gradient mechanical metamaterial for impact mitigation based on the honeycomb configuration within a bridge anti-collision structure. The structure comprises two functional units. The upper layer is a high-porosity energy-absorbing layer, which achieves efficient and gradual release of the initial impact energy through a gradient distribution of porosity and wall thickness. And the lower layer is a high- stiffness support layer that provides the necessary structural support and impact resistance, ensuring stability of the whole structure under extreme loads. By the welldesigned composite gradient honeycomb configuration of the proposed mechanical metamaterial, the anti-collision structure acquires exceptional mechanical properties such as lightweight, high energy absorbing efficiency, and enhanced impact cushioning performance. This research investigates a feasible design of the dual-layer graded honeycomb metamaterial through theoretical modeling and numerical simulations. By conducting dynamic compression and drop hammer impact simulations, the dynamic impact response and energy-related performance are comprehensively studied. Results indicate that the proposed dual-layer composite gradient mechanical metamaterial can significantly reduce the impact load, enhancing both the energy absorption efficiency and safety, therefore advancing the construction of the bridge anti-collision structure.
Development of Sustainable, Multifunctional, Advanced and Smart Hybrid Solid-State Electrolyte for Structural Battery Composites
The incremental energy demand exacerbates the global warming issues. One of the main triggering factors for this raising global warming is the transportation sector which causes around 30% of the world’s emission. However, EU is facing several obstacles to reduce air pollution and greenhouse gas (GHG) emissions caused by the transportation sector. Among these, safety concern in clean technologies is considered the bottleneck to implement emission free technology in transportation sector. Evaluating this obstacle, it was envisaged to run cars with electric power by the rotational power from the electric motor and avoiding the power from internal combustion (IC) engine. Hydrogen (H2) powered IC engine emits airborne pollutants like other IC engines. To resolve this issue, H2 powered fuel cell electric vehicle (FCEV) ensures the clean and emission free transportation dreams into reality. The enormous merits of FCEV including zero emission, faster fueling, noise free drive, economy and higher efficiency (⁓ 60%) are however restricted mainly due to the storage and safety challenges associated with the H2. The Robert Gordon University supported Catalyst Fund project addresses the existing challenges in designing a safe and smart material development for H2 storage tank by adopting innovative technologies for: (a) enhancing volumetric mass of H2 under pressurized condition; (b) a light weight energy storage by further expanding storage of electrochemical energy in structural component without carrying any physical battery; (c) continuous monitoring of damage or leakage due to fatigue/impact; and (d) detection of accumulated moisture hazard well in advance before it causes any unwanted accidents. A composite polymer electrolyte (CPE) as a solid-state electrolyte (SSE) utilizing Li, La, Zr -oxide (LLZO) nanofiber has been synthesized, and the multifunctional property of this new LLZO reinforced CPE is thoroughly under investigation now. The synthesis process and the initial study results are highlighted here. However, the intervening capability of this smart vessel material is the unique characteristic which is missing in the current state-of-the-art technologies. This report initiates this proof of concept in relation to comprehensive integrated power and safety features of H2 pressure vessel which will overcome the obstacles faced by the current FCEV technology and fostering emission free transportation.
Development and Manufacturing of Multifunctional Structures for Automotive Vehicles
The introduction of electric vehicles (EVs) has posed many technical challenges to the automotive industry. State-of-the-art lithium-ion (Li-ion) batteries add significant weight to vehicles both in the form of battery weight and supporting systems, immensely hindering vehicle performance and efficiency. A primary source of the penalty is that battery packs and supporting systems each serve only one purpose, e.g. energy storage or cooling, as current EV battery cells do not carry structural loads or absorb collision impact energy. The Department of Energy Vehicles Technologies Office (DOE VTO) has interest to research, develop, and validate a new class of multi-functional (composite) materials and structures. To meet the DOE requirements, the team lead by Acellent Technologies Inc. along with Senvias has proposed the development of an innovative energy storage solution. The solution utilizes Multifunctional Energy Storage Composite (MESC) structures originally developed at Stanford University and exclusively licensed by Acellent that: A. Supplies electrical power, while serving as a structural element, capable of carrying mechanical loads with reduced weight and complexity B. Uses embedded sensor networks to monitor the state and health of both the composite structure as well as the battery (Battery Health Monitoring, BHM) on a real time on demand basis MESC’s are an optimized design of a robust multifunctional hybrid-composite-batterychassis system that can carry structural loads, safely store electrical energy, absorb impact energy, and lead to significant weight savings. The goal of the DOE project is to develop the end-to-end manufacturing process for MESC structures to be used in ground vehicles. A composite enclosure together with the MESC will be designed and prototyped by the end of the project. This paper will provide the details of the development for the project towards the design of the MESC and BHM for use with an automobile enclosure.
Embedded Piezoelectric Sensing for Metallic Components: A Novel SHM Architecture for Self-Aware Structures
This study presents a novel structural health monitoring (SHM) architecture for selfaware structures based on hybrid manufacturing for fabricating multifunctional metallic components with subsurface-embedded piezoelectric (PZT) sensors, enabling autonomous SHM capabilities. The developed methodology integrates Wire Arc Additive Manufacturing (WAAM), subtractive machining, and Cold Spray Additive Manufacturing (CSAM) to seamlessly embed commercial off-the-shelf PZT sensors within aluminum structures (Al-5356) while preserving structural integrity. The embedded sensors are protected by ceramic thermal barriers and metallized surfaces, facilitating process resumption for complex geometries. The experimental validation is based on acousto-ultrasonic guided wave (GW) propagation analysis under controlled mechanical loading (0-100 MPa) and thermal cycling (ambient to 100oC). Active-sensing configurations utilizing pitch-catch methodologies demonstrate equivalent performance to surface-mounted sensors, with capacitance-based electromechanical impedance signatures confirming sensor functionality. The fabricated self-sensing structures exhibit realtime diagnostic capabilities for detecting operational (static load) and environmental (temperature) state changes, demonstrating particular advantages for applications where surface-mounted sensors are impractical due to coating requirements or environmental protection constraints. This hybrid manufacturing framework establishes a foundation for next-generation intelligent metallic structures with integrated SHM functionality.
Damage Detection in Composite Aircraft Fuselages Under Load and Temperature Using Gaussian Processes for GW-SHM
A full-scale composite door surrounding aircraft structure was instrumented with a GW-SHM system and subjected to three representative quasi-static load cases using a hydraulic test rig and broad temperature variations throughout the experiment. A data- driven approach based on Gaussian Processes is used to detect barely visible impact damage introduced during the test campaign, differentiating between benign environmental/operational conditions and barely visible impact damage.
Modeling the Power Generation from Multi-Cell Metamaterial Energy Harvesters
Continuous Structural Health Monitoring (SHM) is vital for ensuring the safety of aerospace and naval structures by reducing maintenance costs. While SHM systems typically rely on sensors, they require continuous power, often supplied by batteries or on-board power source. This may increase the power demand with the growing number of sensors. To address the energy demand, localized energy harvesting offers a promising alternative by enabling self-powered sensor networks. This study introduces a novel metamaterial energy harvester designed to capture ambient vibrations during various flight phases to power SHM sensors. Existing vibration-based energy harvesters are not optimized for changing operation conditions. The proposed design addresses this gap using a metamaterial-inspired configuration that operates efficiently in a broader frequency range between 800–1200 Hz range. This frequency range aligns with the power needs of typical SHM sensors, such as ultrasonic actuators requiring approximately 500-800 W. A comprehensive numerical study using Multiphysics simulation tools evaluates and optimizes the harvester’s power output. These results confirm the harvester’s capability to power SHM sensors reliably, significantly reducing battery dependence. This work demonstrates a feasible and efficient energy harvesting approach, supporting the development of self-sustaining SHM systems for aerospace applications and contributing to greener, more autonomous aircraft maintenance technologies.
Airbus Strategy and Use Case for Airframe Structural Health Monitoring of Aeronautical Structures Dedicated to Damage Detection Assessment and Damage Monitoring
Airbus is engaged in the development of Structural Health Monitoring solutions to reduce maintenance costs. SHM solutions for damage detection are particularly under investigation as alternatives to “classical” NDT tasks, to alleviate the maintenance burden. These damage detection systems are intended to be installed on the aircraft and interrogated at dedicated checks in the life of the aircraft. Such configurations can be seen as an “NDT-in-situ” SHM approach, where the NDT Probe is permanently installed. In the case presented here, a damage detection and monitoring performance demonstration shall be performed to ensure the detection performances and measurement accuracy of the SHM solution along the life of the aircraft, thus including the effects of environmental and mechanical ageing. The Probability of Detection study applied to SHM solutions dedicated to damage detection will be detailed including the method for identification of all variables which could influence the detection sensitivity. The function describing the crack length measurement accuracy beyond detection capability is introduced. In this presentation the performance demonstration approach developed at Airbus will be presented, as well as results obtained on a specific use case. The first part of the presentation will show the qualification process developed for this purpose, including how the effects of sensor aging are considered with respect to probability of detection and accuracy of measurement. In the second part of the presentation, one use case application will be presented. It will be explained how SHM sensors are expected to replace classical NDT tasks and to avoid heavy maintenance operations for accessing the area to be inspected with classical NDT methods. Finally, the last part will focus on the value of embedded SHM sensors on aeronautical structures: the damage monitoring. The requirements associated with this use case will be detailed and Airbus methodology for demonstrating its capabilities through the test plan.
Modeling Ultrasonic Wave Propagation in Prismatic Li-ion Battery Cells
Accurate in-situ monitoring of key states in prismatic Li-ion battery cells, such as temperature, state of charge (SoC), and state of health (SoH), is critical for ensuring the safety and performance of electric transportation. Since these states are closely linked to the cells’ internal mechanical properties, ultrasonic non-destructive testing (NDT) has emerged as a promising diagnostic approach. However, practical implementation remains challenging due to the complex nature of ultrasonic wave propagation within the cells’ internal structure, which comprises stacked layers of porous electrodes and separators. While analytical methods like the transfer matrix method and Biot theory are widely used to model wave propagation in layered and porous media, they fall short in capturing the behavior of porous layers with heterogeneous solid frames. To address this, the present study develops finite-difference time-domain (FDTD) models that account for both inter-layer wave interactions and micro-scale fluid-solid coupling within the porous layers, enabling simulation of wave propagation through the thickness and along the plane of the cell layers. The simulation results are analyzed and compared with experimental data, and the resulting insights provide a deeper understanding of ultrasonic wave behavior in battery cells, laying a solid foundation for advancing ultrasonic-based battery state monitoring.
Matched Filtering-Based Sensor Fault Classification for Structural Health Monitoring Systems
Structural health monitoring (SHM) systems rely on sensor networks to assess the condition of engineering structures designed for long-term operation. Over time, structures such as wind turbine towers or bridges may experience degradation-related damage, while the monitoring systems themselves age and become less reliable. This aging can lead to sensor faults that generate plausible yet inaccurate data, potentially causing misinterpretations of structural integrity and even catastrophic failures. Therefore, distinguishing between sensor faults and actual structural damage is crucial for ensuring the long-term reliability of SHM systems. A key challenge is the diversity of sensor fault types, which must first be classified to enable effective compensation. This study introduces a matched filtering approach to address this issue. Sensor fault types, such as bias and drift within a sensor network, are analyzed using matched filtering to classify them effectively. The proposed classification method is validated on a real-world support structure with synthetic measurement data, a 9-meter-high lattice mast exposed to real environmental conditions. The results demonstrate that sensor faults can be accurately classified. A key advantage of this approach is that precise classification enables more accurate sensor fault compensation. Enhancing the robustness of SHM systems will significantly improve the reliability of data-driven structural assessments, a crucial aspect for ensuring the longevity of critical infrastructure.
Reliability Assessment of Non-Destructive Inspection Using Additively Manufactured Artificial Defect Specimens
Various non-destructive inspection (NDI) techniques are being utilized to detect cracks early for the flight safety of aircraft structures, but it is difficult to obtain actual cracked parts to verify them. In this study, artificially defective specimens were manufactured using 3D printing technology to solve this problem. The manufactured specimens were subjected to various destructive/non-destructive tests, including eddy current testing, to quantitatively evaluate the detection performance according to the defect size of the specimen. The results of this study can be applied to the reliability evaluation of NDI and structural reliability analysis, and in particular, it is expected to contribute to overcoming the difficulties of experimental data collection and developing a highly reliable defect detection model.
Decentralized Sensor Fault Diagnosis for Wireless Structural Health Monitoring Systems Using Artificial Intelligence of Things
Structural health monitoring (SHM) is a non-destructive evaluation technique that utilizes sensor data for assessing the condition of civil infrastructure. Sensors in SHM systems may experience faults, which may influence the accuracy, reliability, and performance of SHM systems. The timely detection, isolation, and accommodation of sensor faults in SHM systems has been the focus of sensor fault diagnosis (FD) approaches, which have increasingly been employing artificial intelligence (AI) algorithms due to the effectiveness of AI in sensor FD. However, current AI-based FD approaches require transmitting large amounts of raw sensor data to centralized servers for offline analysis, resulting in inefficiencies as well as computational burdens on centralized servers. This paper introduces a decentralized sensor fault diagnosis (DSFD) approach for wireless SHM systems using Artificial Intelligence of Things (AIoT). In particular, AI-based FD models are embedded into wireless sensor nodes of SHM systems to detect, isolate, and accommodate sensor faults. By embedding the FD models into the wireless sensor nodes, only high-level information, specifically the status of the sensors, is transmitted to centralized servers. As a result, data transmission inefficiencies as well as computational burdens on centralized servers are reduced. The proposed DSFD approach is validated in a controlled laboratory experiment, in which custom- built wireless senor nodes are installed on a test structure that is dynamically excited using a shake table. After training and embedding the AI-based FD models into the custom-built wireless sensor nodes, sensor faults are artificially injected into the sensor data, demonstrating the ability of the DSFD approach to diagnose sensor faults in a decentralized manner. The results of the validation test corroborate the capability of the proposed approach to efficiently ensure the accuracy, reliability, and performance of SHM systems.
Designing Likelihood Function in Block Particle Filter for Beating Curse of Dimensionality
Particle filter (PF) has proven effective for nonlinear identification scenarios; however, its performance in high-dimensional problems is often limited by the curse of dimensionality. To overcome this challenge, block particle filter (BPF) is proposed to reformulate a high-dimensional model into several blocks, so the identification of one high-dimensional system can be simplified into that for many lower-dimensional blocks. However, due to the coupling between blocks, the likelihood function for each state subgroup depends not only on its own state components but also on its neighboring subgroups - a dependency that the BPF does not address. In this work, we extend the BPF by developing likelihood functions that incorporate nuisance components, thereby enabling its application to coupled systems. The performance of the proposed algorithms is demonstrated through a numerical example of a forty-story Bouc-Wen frame structure subjected to ground motion.
Development of Sensing Techniques for Material State Awareness in Natural Fiber Composites
Natural fiber reinforced composites (NFRCs) offer a potential alternative to carbonfiber composites in some aeronautics applications. Active health monitoring may enable broader use of NFRCs by providing the material state awareness needed to reduce design knockdown factors and to monitor for environmental degradation in service. This paper describes initial steps taken toward development of dielectric and acoustic in-situ sensing approaches that would allow moisture and material property changes to be monitored in real-time. Coupons of three different thermoplastic NFRC material systems were exposed to accelerated moisture absorption and changes in properties relative to baseline values were assessed. Strong correlation in both dielectric and acoustic properties with moisture level were observed, with dielectric properties showing reversible, monotonic change, while some acoustic properties changed irreversibly.
Screen-Printed Sensor Inlays for Monitoring Bondlines in CFRP-Components
Adhesive bonding is a promising approach for joining lightweight composite components. However, the reliability remains a major challenge, because failures due to fatigue are difficult. In this study, we report on the integration of a screen-printed sensor on polyetherimide substrate into the adhesive layer during CFRP fabrication, enabling in-situ monitoring of potential crack propagation due to internal strain measurement under service loading. The sensor concept is based on the comparison of strain measurements in two different sensor rows to detect deviations in the sensor signal that indicate damage. This work marks the first successful fabrication by screenprinting of a fully polyetherimide-based sensor inlay that exhibits excellent adhesion to the epoxy matrix in CFRP. We used carbon-based inks that withstand the harsh conditions during the co-curing during the CFRP-prepreg-autoclave process. After the integration process, the sensors remained fully functional and achieved a gauge factor of up to 49.
Terahertz Inspection as a Unique Enabler for Characterizing Sustainably Derived Composites
The Sustainable Manufacturing of AirCraft (SUMAC) project at NASA is developing composites from naturally derived fibers and resins for aerospace applications, with a specific focus on advanced air mobility (AAM). Naturally derived composite materials have the potential to reduce carbon emissions in aviation manufacturing and create new markets. Naturally derived composites for AAM use can be made economically viable by using structural health monitoring (SHM) to reduce knockdown factors, to provide data for condition-based maintenance, and to increase the range and payload capacity for the AAM vehicles. Composites made from naturally derived fibers such as hemp and flax could also provide strong vibrational damping in vehicles and aircraft compared to carbon-based composites. However, the same vibrational damping also makes naturally derived composites difficult to inspect with ultrasonic testing (UT). Terahertz (THz) radiation sits between microwave and infrared frequencies in the electromagnetic spectrum, and it has been used to characterize and inspect lossy dielectrics like thermal foams and biological materials such as plants. Unlike traditional composites, naturally derived composites are non-conductive and are thus penetrable by THz radiation. THz inspection is not used as widely as more “traditional” inspection techniques like UT and X-ray computed tomography because of its relatively new availability on the market. However, it offers sub-millimeter resolution imaging and unique spectral information while being non-ionizing and low power emission. THz can give specific information about the chemical makeup of naturally derived composites while also measuring density and thickness. THz time-domain spectroscopy (THz- TDS) can be used to interrogate different layers of a material by analyzing the THz reflections arriving at different times. THz is highly attenuated by water and responds spectrally differently to liquid and vapor water. This sensitivity to water can enable THz-TDS inspection to be used as ground truth or a calibration point for SHM sensors for detecting moisture uptake in structures. Our study examining the response of natural composites to THz radiation could enable future integration of THz sensors into vehicles for SHM. The unique capabilities of THz for inspecting and characterizing novel naturally derived composites at NASA will be presented. THz inspections were explored on a variety of flax-reinforced thermoplastic composite samples and individual constituents. This work investigates the alignment between composite layers, moisture sensitivity and moisture uptake mechanisms in the composites, and mapping of the ratios of fibers and resins in the composites as a first step toward applying THz for periodic monitoring of natural composite structures in AAM vehicles.
Real-Time Evaluation of Impact Induced Delamination in UAV Composite Structures Using Fiber Optic Sensors
This study focuses on detection and evaluation of low-velocity impact induced delamination in the thick CFRP laminates used in main structures of UAVs. When the low-velocity impact occurs on the structure during operation or maintenance, acoustic emissions are generated, which are important clues for detecting and evaluating damages caused by the impact. In particular, the real-time detection of delamination using on board sensors is essential for safe operations of structures because the delamination is invisible and hard to be detected by conventional nondestructive methods. In this study, fiber Bragg grating (FBG) sensors were used for measuring the acoustic emission signals generated from the impact. Test articles are CFRP laminates with the thickness of 5 mm, and the FBG sensor was surface attached on the bottom surface of each specimen. The low-velocity impact was given to each specimen with different energies by controlling the drop height. The acoustic emission signal in each case of different impact energy was captured by the FBG sensor in the sampling frequency of 100 kHz. This sampling speed is fast for FBG sensors, but is generally considered to be a limited speed for damage evaluations. By C-scan inspections after low-velocity impact experiments, delaminations were found in the impacted specimens over the impact energy of 15 J. The measured signals have a form in which the main waveform generated by the structural deformation and the acoustic emission signals are mixed. The signal processing method is proposed for detecting and evaluating the delamination using such measured signals from FBG sensors. This method consists of two steps: delamination detection and delamination area prediction. Strong acoustic emission signals from the delamination occurrence affect the waveform of the measured signal, so the delamination could be detected by analyzing the symmetry of the main waveform of the measured signal. Through this first process, signals that are determined to have been damaged are selected. As the next step, the delamination areas were estimated using the induced peaks of acoustic emission signals during generating the delamination in the laminates. In this study, the delamination areas were measured by a linear regression using the counted numbers of peaks related to delamination. As a result, the delamination area could be reasonably evaluated using the method proposed in this study. Through this study, a useful method for a real-time delamination diagnosis with a simple optical fiber sensing system was proposed, which is considered to be a practical impact damage monitoring system applicable to real composite structures.
Vibration and Modal Analysis for Damage Detection in Cantilever Beams: Assessing Presence, Location, and Severity Through Numerical and Experimental Approaches
Ensuring the reliability and safety of engineering structures requires an accurate assessment of structural damage. This study employs analytical, computational, and experimental techniques to detect and evaluate the presence, location, and severity of damage in a cantilever beam. A vibration-based approach is utilized, focusing on key modal parameters such as natural frequencies and mode shapes to assess structural integrity. And to simulate the beam’s dynamic behaviour under various damage conditions, a finite element model (FEM) is developed. Experimental validation is carried out through vibration testing, where frequency shifts and mode shape variations are recorded for different damage locations and depths. The results indicate a strong correlation between damage severity and changes in modal properties, with a noticeable decrease in natural frequencies and alterations in mode shape curvature as damage intensity increases. A total of 19 numerical and experimental case studies are conducted, achieving approximately 90% accuracy in identifying the presence, location, and severity of damage. This study also presents a comparative analysis of numerical predictions and experimental findings, demonstrating the effectiveness of frequency-based damage assessment. The proposed approach offers a reliable and scalable method for structural health monitoring, making it suitable for real-world engineering applications. These findings contribute to the advancement of damage detection techniques, enabling early intervention and enhancing overall structural safety.
Microcrack Testing and Monitoring Using FBGS for Hydrogen Tanks
The transition toward zero-emission aviation demands the development of lightweight and safe storage systems for cryogenic hydrogen. Type V composite tanks, which lack a metallic or polymeric liner, offer significant advantages in terms of mass reduction and thermal compatibility. However, their longterm viability is challenged by hydrogen permeability due to matrix microcracking, which can occur under cryogenic conditions and compromise tank integrity. This study explores an indirect method to assess microcrack formation by measuring the thermal expansion behavior of a composite laminate. Using controlled thermal cycles and induced damage scenarios, changes in the coefficient of thermal expansion (CTE) were monitored and correlated with internal microcracking. Results demonstrate a measurable and repeatable variation in thermal expansion after damage, supporting its use as a structural health monitoring (SHM) indicator. The findings provide a promising path toward the implementation of non-destructive techniques for in-service integrity assessment of Type V hydrogen tanks.
Comparison on Sensor Fault Detection Techniques for SHM Systems
Engineering systems are designed to serve society for decades and are exposed to multiple environmental and load conditions. This circumstance leads to the degradation of the system, causing different structural behaviours. In this regard, monitoring systems can assess structural health by recording time-dependent structural behaviour through a network of measurement instruments. However, monitoring systems can be affected by changing external conditions, leading to sensor degradation and faults that produce plausible data but incorrect information about the structural condition. Therefore, distinguishing between sensor faults and structural damage is critical to ensuring the reliability of SHM over the lifetime of the structure. Different techniques with varying effort and complexity could be used, such as linear regression, Mahalanobis distance, or multiple neural network architectures, to identify sensor faults. This study investigates the suitability of these techniques regarding the accuracy of sensor fault detection and computational effort on the example of a steel specimen equipped with nine strain gauges in a 3-point bending test with a cyclic load. In the test, one of the nine sensors showed a signal drift, which needs to be detected in a monitoring application using these sensor fault detection techniques. The results show that for this simple test, the Mahalanobis distance is the fastest and most accurate, while the linear regression technique is very fast but very imprecise, and the artificial neural network is relatively accurate but very slow. However, all the techniques used showed the potential to significantly increase the robustness of the monitoring system, which is important for more complex engineering structures. Keywords: Artificial Neural Network; cyclic loading; linear regression; Mahalanobis distance; sensor ageing; sensor fault detection; strain gauge measurement; Structural Health Monitoring
Application of 1D iFEM to an Experimental Beam Model with Degrading Boundary Conditions
The inverse Finite Element Method (iFEM) is a robust computational technique that utilizes a network of strain sensors to reconstruct full-field displacement on beam or shell structures. Importantly, the method is independent of specific loading conditions and material properties, making it particularly suitable for real-time structural monitoring and assessment. By providing a comprehensive understanding of structural behaviour, iFEM enables the implementation of condition-based maintenance policies, which are crucial for enhancing the operational lifespan of engineering structures. This study evaluates for the first time the effectiveness of 1D iFEM strategy in shape sensing of an experimental beam structure with degrading boundary conditions. In addition, a dedicated procedure has been implemented to reconstruct the percentage reduction in the stiffness of the degraded constraint, which is essential for structural health monitoring and for defining appropriate retrofitting strategies to be applied to the structure. The results show the potential of iFEM for structural health monitoring and adaptive maintenance strategies in real-world applications, where boundary conditions may evolve over time.
Exploring Compressed Sensing Approaches for ToA Estimation in Thin Composite Plates
Guided Wave-based Structural Health Monitoring (GWSHM) systems make a promising technique for monitoring and evaluating the integrity of many industrial structures like pipelines, aircraft, and civil structures. Composite structures used in aircraft must continue in flight-safe conditions when subjected to various levels of damage and operating conditions, such as stress, load, temperature, humidity, during manufacturing and operational usage. Even though the GWSHM system has the capability to observe the structure integrity continuously, it requires a significantly high sampling rates that become a burden while recording and transmitting the data for continuous monitoring. Recording data with a high sampling rate results in high energy consumption during transmitting/uploading, resulting in a bottleneck issue for modern embedded SHM systems that may be battery-operated and/or have a wireless connection. One possible solution is applying compression during the acquisition stage, thereby keeping only the relevant information. This article proposes a compressed sensingbased framework for estimating the time of arrival (ToA), which is a key parameter in signal recovery, damage detection, localization, and imaging applications. In this framework, the ToA is estimated by solving a sparse deconvolution problem with Orthogonal Matching Pursuit (OMP), investigating different sparsity-providing bases such as Discrete Fourier Transform (DFT), Discrete Wavelet Transform (DWT), and Discrete Cosine Transform (DCT). These bases can be combined with subsampling strategies for compression. For recovery, several grid-free techniques such as the Atomic Norm Minimization (ANM) and parametric high-resolution ToA estimators such as Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT), are explored. The methods are tested on synthetically generated data as well as experimental measurements on composite plates. The results are compared to traditional methods for ToA estimation using the maximum peak of the envelope of the Hilbert transform of the received signal, providing a complete framework for resource-efficient ToA estimation in SHM applications.
Effect of Cryogenic Thermal Cycling on the Response of a Fiber Bragg Grating Sensor
The manufacturing and use of composites hydrogen tanks for transport applications faces a series of very important challenges among which one of the most critical is the definition of a structural monitoring system that is able to adapt to the conditions present in the tank. Among the different ways of storing H2, two of the most interesting from the point of view of gravimetric and volumetric density are the storage of H2 in liquid form LH2 or in the form of cryo-compressed gas CcH2 in both cases the tank structure (either the liner for class III and IV or the composite structure itself for class V tanks) must be able to withstand very important thermal variations in temperature during the processes of filling and emptying the tank, i.e. able to withstand high thermal cycling requirements. The research work carried out has focused on studying the impact of such thermal cycling on the sensor response. For this purpose, FBG sensors have been integrated in several coupons that have been subjected to multiple thermal cycling (20ºK-80ºK). After each thermal cycling, a reference tensile test has been performed on the coupons, recording the responses of the sensors to see the effect of the accumulation of the cycling, in parallel after each cycling, inspections have been performed by micro computed tomography to try to check if the possible cause of the variation in the response is associated with micro cracking in coupon-sensor interface. The final objective of the research is to ensure the reliability of the response of the FBG sensors for H2 SHM application.
An Obstacle Intrusion Detection Method for Complex Environment Tracks Based on Manifold Regularization
Tracks are essential infrastructure for high-speed railroads and are the key to the safe passage of trains. Once obstacles intrude into the track area, it will threaten the safety of train operations. Therefore, there is an urgent need to propose a fast and efficient obstacle intrusion detection method. However, it greatly challenges obstacle intrusion detection in the complex natural environment (rain, fog, light changes, etc.). To this end, this paper proposes an obstacle intrusion detection method for complex environment tracks based on manifold regularization. This method uses the single-stage object detection framework YOLO as the basic structure, integrates the multi-head attention mechanism to improve the object detection performance, and uses the dilated convolution structure to reduce the model parameters and improve the detection efficiency; in the feature extraction space, it introduces the manifold regularization constraint to realize the alignment constraint of various categories of features under different natural environment images and improves the generalization performance of the detection model. The results show that the proposed method can adapt to various complex natural environment detection conditions and effectively improve the accuracy and generalization of the obstacle intrusion detection method.
Uncertainty Laws of Free-Vibration Modal Identification
Free vibration test is a commonly used method in structural condition assessment to acquire vibration data from which modal parameters (e.g., natural frequencies, damping ratios, and mode shapes) can be extracted. This naturally leads to a fundamental question: how a field test can be planned optimally? Focusing on modal identification under free vibration, the paper addresses this question by proposing uncertainty laws, which give closed-form asymptotic formulas for coefficients of variation of modal parameters. These laws characterize the relationship between the identification uncertainties of modal parameters and the test configuration (e.g., location, number and noise level of sensors). With the assumptions of long data and small damping, the derivation involves the Fisher information matrix evaluation. Theoretical results and assumptions are validated using synthetic data. According to the derived uncertainty laws, the key factors governing identification uncertainty include the damping ratio, the bandwidth factor, the modal signal-to-noise ratio, and the effective data length. The developed uncertainty laws provide a scientific basis for managing identification uncertainties in free vibration tests.
Exploring Feature Extraction Strategies for In-Situ Fault Detection of a Metal Additive Manufacturing Process with Time-Series Detection Models
Metal additive manufacturing (AM), particularly through Laser Powder Bed Fusion (LPBF), enables the fabrication of complex geometries with applications in aerospace, automotive, and other high-performance industries. These components must often meet stringent requirements for durability, weight, and cost. However, the AM process remains susceptible to internal defects such as overmelting and spatter, which originate in the melt pool and adversely affect part quality. Real-time monitoring of the melt pool using imaging sensors has emerged as a critical approach for detecting such defects, though the high dimensionality of image data necessitates data compression to enable efficient, real-time anomaly detection. This work investigates and evaluates various image compression strategies for their effectiveness in supporting unsupervised, real-time anomaly detection within the LPBF process. Building upon a predictive model trained on a healthy baseline, the detection algorithm identifies faults by assessing deviations in incoming signals. The study reveals that certain compression methods may obscure these deviations, limiting detection sensitivity. Therefore, an appropriate compression technique is essential to maintain detection performance while reducing computational complexity. Both physically intuitive features such as melt pool dimensions and machine learning-based feature extraction methods are examined. The efficacy of each approach is evaluated across several defect types commonly encountered in metal AM builds. These results assess the anomaly distinguishability of each method, offering guidance for the development of robust in-situ fault detection for metal AM.
Detection of Hidden Fatigue Crack Growth in Single-Sided Fastened Joints
Aircraft structural elements are usually assembled using single-sided fasteners— pins with flush heads on one end and threads on the opposite end coupled with collars designed to ensure specific torque levels. Common examples include Hi-Lok and Hi- Lite pins. Unfortunately, fastened joints also provide unique opportunities for hidden damage, including fatigue cracks, corrosion and fretting, due to stress concentrations at the hole, friction between fasteners and surrounding material, and moisture that can ingress and become trapped between layers. The complex multi-layered geometry at these joints also forces disassembly to inspect using conventional techniques. Many proposed structural health monitoring (SHM) approaches tackle this problem by modifying the installed condition of the fasteners by placing a sensor between the structure and the collar or fastener head. This paper presents a novel SHM approach that can detect hidden fatigue cracks in multilayer fastened structures without disassembly or modification to the installed condition of the fastener. Piezoelectric elements are integrated post-installation to turn the fasteners themselves into impedance sensors. The coupled electro-mechanical resonance changes in the presence of local damage, which softens the adjacent boundary condition constraining the fastener. Experimental data illustrates a detection capability for finding hidden fatigue cracks of at least 1.5 mm.
Underwater Fish Monitoring Through Vortex-Induced Vibration Sensing
Underwater fish and marine animal monitoring is important in assessing biodiversity and the health of aquatic ecosystems as well as for understanding the impacts of offshore structures on those animals. Existing methods, such as acoustic sensing, tagging, flow field sensing, and optical imaging, are often energy-intensive, invasive, fragile, or dependent on lighting conditions. Inspired by the sensory mechanism of seal whiskers, we introduce an energy-efficient, non-invasive marine animal monitoring method based on vortex-induced vibration (VIV) sensing. The main intuition of our method is that as marine animals swim, they generate characteristic vortex patterns and turbulence that propagate through water. By detecting these VIV signals, our system can identify the movement of these animals. A key challenge lies in capturing these low-amplitude, rapidly dissipating vortices. To address this, we design a flexible sensor mounting structure in which the sensor is suspended by a string from a fixed base, allowing adaptive movement in response to vortex-induced flow. We validate the system in a water tank using swimming fish toys and other objects to induce vortices, achieving a classification accuracy of 96.88%, demonstrating its effectiveness and potential for monitoring underwater fish and marine animals.
Non-Contact Monitoring of Rotating Machinery Using Optimized Camera-Based Stroboscopic Imaging
Monitoring rotating machinery is essential for ensuring reliability and reducing maintenance costs. Cameras have recently gained attention as non-contact sensors due to their high spatial resolution and minimal interference. However, high-speed rotation introduces challenges such as motion blur, aliasing, and reliance on expensive highspeed cameras. This study proposes a camera-only stroboscopic imaging method for real-time, non-contact monitoring. By capturing frames at fixed angular intervals without external synchronization and applying optimized object tracking and phasebased optical flow (POF), the system estimates full-field, sub-pixel displacements. Phase-locked displacement data is used to identify rotor faults. Experimental validation on a lab-scale blade system confirms accurate displacement measurement and effective fault detection, offering a practical solution for aerospace, wind, and industrial applications.
Multi-Sensor Fault Diagnosis of Wind Turbine Gearbox Via Ensemble Filter Network
The wind turbine gearbox (WTG) is regarded as a pivotal component in modern power generation systems, primarily due to its capability to streamline structural complexity and optimize energy conversion efficiency. Nevertheless, the transmission systems are frequently subjected to premature degradation, which stems from prolonged exposure to stochastic loading patterns and extreme environmental stressors. To address this reliability challenge, a set of symptom parameters is systematically extracted from different aspects. Then, multiple symptom parameters are fed into the proposed ensemble filter network to achieve the precious and robust fault diagnosis of wind turbine gearbox. Experimental validation was conducted on the WTG fault simulation test platform of Beijing Jiaotong University. The results show that the proposed method is competitive with other existing methods in terms of diagnosis accuracy and performance stability.
Automated Rebar Recognition and Evaluation Through Hyperbolic Fitting and Signal Analysis of Ground Penetrating Radar GPR Data
Precise rebar spacing and depth determination are essential for evaluating the integrity and load-bearing capacity of reinforced and prestressed concrete structures. Traditional manual interpretation of ground penetrating radar (GPR) data can be timeconsuming and inconsistent, particularly when dealing with complex radargram signals. This study presents an automated rebar-picking method that employs hyperbolic fitting and positive peak signal analysis to enhance the accuracy of rebar detection. The developed algorithm processes GPR data by identifying hyperbolic reflections and extracting amplitude values from the peaks of these hyperbolas. It then compares these values to theoretical hyperbolas. A user-defined threshold is incorporated to enhance reliability, enabling users to adjust acceptable deviations. The algorithm constrains the difference between the detected hyperbolic amplitude patterns and the theoretical hyperbolas to 80 percent, accommodating a certain level of noise or signal variations. Once a rebar is identified, the algorithm automatically determines its location. Controlled experiments were conducted on a prestressed concrete beam to validate the method, during which GPR data was collected and analyzed. The results show that the algorithm achieves over 90 percent accuracy, successfully detecting and localizing most rebars. Additionally, field scans of prestressed concrete box beams confirm the efficacy of this method in determining rebar spacing and depth, even in challenging conditions involving signal noise or overlapping reflections. The findings demonstrate that this proposed method significantly enhances GPR non-destructive evaluation techniques by improving efficiency, precision, and repeatability while reducing the need for manual interpretation. Furthermore, the study highlights the potential of automated hyperbola based GPR analysis in structural health monitoring, offering a reliable and scalable solution for rebar detection in reinforced and prestressed concrete bridge infrastructure.
Monitoring of Hydraulic Structures: Long-Term Testing of Inclination Sensors
Structural health monitoring of massive hydraulic structures is becoming increasingly important in Germany due to the age structure and condition of the infrastructure necessary to ensure safe shipping. A survey within the Federal Waterways and Shipping Administration (WSV) has shown that inclination sensors are most frequently used in the monitoring systems of massive hydraulic structures. However, inclination sensors are prone to drift, which can lead to misinterpretation of measurement data, especially over long measurement periods. The potential of the inclination sensors on the one hand and their deficits on the other hand show the necessity to examine their behavior under long-term testing conditions. Eight potentially suitable inclination sensor models are tested under typical and comparable environmental conditions. Since each sensor is unique, three identical single-axis inclination sensors of each model are tested. The test rig is designed for long-term stability. So far, the 24 inclination sensors have been measuring for almost 2 years in a closed concrete block under the open sky to replicate typical environmental conditions (temperature, solar radiation, etc.). Various temperature sensors installed in the concrete block ensure compensation for temperature effects. An automatic and a manual reversal measurement are used as references because they detect and eliminate their own linear sensor drift. In addition to explaining the design of the test rig, an interim result is presented that identifies the inclination sensor models that show the lowest sensor drift and sufficient long-term stability.
Noise Tomography Based Urban Ground Collapse Monitoring Using Distributed Acoustic Sensing
With extensive development of urban underground space, ground collapses occasionally occur, resulting in property damage, injuries, or fatalities. Therefore, establishing an effective ground monitoring and collapse prevention system is necessary for reducing collapsing risk. Distributed Acoustic Sensing (DAS) technology has great potential for ground collapse monitoring around rail system for capacity of long-distance high-density monitoring and immunity to electromagnetic interference (EMI). In this study, experimental investigations were conducted using a high-performance DAS interrogator integrated with a 4 km-long distributed optical fiber to record background ambient noise inside a subway tunnel. Noise2Noise (N2N) unsupervised denoising model was employed to preprocess the DAS data across different time spans, thereby enhancing the signal-to-noise ratio (SNR). Additionally, noise tomography method in terms of passive multichannel analysis of surface waves (MASW) algorithm was utilized to extract the surface waves and calculate the dispersion curves containing soil layer information Stacking methods, including Phase-Weighted Stacks (PWS) and Generalized Average of Signals (GAS), were applied to further improve the SNR of the dispersion curves. Ultimately, a 2D S-wave velocity model to 45m depth was constructed, revealing low-velocity soil defects such as hollow and settlement. The result demonstrates that integrating DAS with tomographic imaging using urban ambient noise can provide a reliable basis for preventing ground collapse hazards.
Enhanced Thermo-Mechanical Decoupling Using Fiber Bragg Grating Sensors in Birefringent Fibers for Structural Health Monitoring
Fiber optic sensors are increasingly used in aerospace applications due to their lightweight, immunity to electromagnetic interference and ability to measure multiple parameters like strain and temperature. Fiber Bragg Grating (FBG) sensors are particularly promising for Structural Health Monitoring (SHM) but face challenges in distinguishing thermal and mechanical effects, as both induce similar wavelength shifts. Current decoupling methods are classified as extrinsic (requiring additional components) or intrinsic (leveraging sensor properties). While extrinsic methods are effective, they increase system complexity. Intrinsic approaches reduce invasiveness but remain underdeveloped for practical applications. This study introduces birefringent optical fibers with inscribed FBG sensors to address these limitations. Birefringence, caused by refractive index asymmetry, generates two distinct reflection peaks with different sensitivities to strain and temperature, enabling effective decoupling. Experimental validation confirmed the method’s accuracy and repeatability. To enhance performance, an optimized resolution strategy was developed, improving measurement accuracy and reducing errors. Machine learning techniques were also explored to refine data analysis. Results demonstrate that birefringent FBG sensors offer a scalable, non-invasive solution for aerospace SHM. Future research will focus on optimizing sensor integration and performance, with an emphasis on collaboration with industry to enable real-world applications.
Utilizing Wireless Meshing Sensors for Structural Health Monitoring
To facilitate the long-range wide area in situ analyte monitoring of a subterranean structure, the US Army Corps of Engineers (USACE) Engineer Research and Development Center (ERDC) has developed a robotics system, the Subterranean Probe for Locating Intrusions, Natural Threats, and Environmental Reconnaissance (SPLINTER), and a sensor agnostic wireless sensor network, the Guardian Node (GN). In support of USACE Missions, there is a need to determine the presence of harmful chemicals before humans enter the area. The SPLINTER is a common robotics system (individual), CRS(I), robot with a sensor payload that enables the robot to perform simultaneous localization and mapping (SLAM) of subterranean systems. This capability allows the robot to create a detailed map of the environment, which is then used to inform the placement of GNs. The GN is a developed in-house sensor node that forms a wireless, meshing, and self-healing ad-hoc sensor network that interfaces with ERDC-developed chemical sensing platforms such as the Army Corps of Engineers Potentiostat (ACEstat), and the in-development quartz crystal microbalance with dissipation (QCM-D) system, as well as commercial off-the-shelf (COTS) systems that utilize standard digital protocols including SPI, I2C, and UART. Together, the SPLINTER deploys the GNs, which then enable a subject matter expert (SME) to perform near real-time analyte monitoring of a subterranean structure.
Damage Assessment of a 3D-Printed Plate Using Capacitance of Surface-Bonded and Embedded Piezoelectric Sensors
This research focuses on damage assessment based on the electromechanical impedance (EMI) method. The method uses piezoelectric transducers that excite the investigated structure and sense the response. The damage assessment is based on comparing the electrical spectra gathered for structural parts at different structural conditions. In particular, the unknown case (possibly damaged) is compared with data for known healthy (undamaged) case. In the reported research the EMI method was applied to additively manufactured samples. For several years, additive manufacturing (AM), or 3D printing, has become a popular manufacturing technique that is environmentally friendly by allowing for waste reduction. The structural parts manufactured with AM methods were introduced into the mechanical and aerospace industries. Similar to structures made from traditional metals or polymers, there is a need for structural health monitoring of 3D printed structures. This requires the development of accurate and reliable methods for evaluating and monitoring the structural integrity of such components. Additionally, the AM method gives more freedom in design and also allows for easier sensor integration for structural health monitoring. In this work, the piezoelectric sensors were embedded in a polymer 3D- printed plate and their response was compared with the surface-bonded sensor. In this study, the effective excitability of the sensors was tested with the scanning laser vibrometer. Due to high attenuation, the EMI investigation was limited to 100 kHz. In total four sensors were used for the sample assessment, and the capacitance as a function of frequency was analyzed. Firstly, the structural change was simulated by an additional mass. Secondly, a through-thickness hole was drilled to simulate damage, and the EMI responses were compared for four diameters of this hole. Traditionally, in the EMI approach resistance, conductance, or impedance is studied. However, in this study, promising results were obtained for capacitance that allowed for damage detection and later for severity assessment. The introduced data processing approach based on principal component analysis (PCA) allowed for the differentiation of all the investigated cases and showed good sensitivity to the simulated damage severity.
PEGASE4: A Generic Board for Embedded SHM Applications
This article aims to present the newest version of PEGASE: a generic wireless board dedicated to SHM applications. The first part of the article consists in describing the technological advances of PEGASE (generic processing using an embedded buildroot Linux, inertial IMU unit, GNSS receivers, multiple IOs...). Then scientific aspects of PEGASE are specified : the original use of the GNSS/PPS signal as a core information inside Linux to make it synchronized up to some μs, the energy strategies that could be adopted in view of implementing the most efficient battery/solar cell usage,... Thus, real SHM use-cases are given as illustrations : PEGASE usage for a wireless acoustic emission monitoring system.
Locating Crack Tip in Mode-I Fracture Tests of Engineered Wood by Acoustic Emission and Machine Learning
As an eco-friendly construction material, engineered wood is becoming increasingly popular as it is environmentally friendly, less labor-intensive and more cost-effective compared with concrete and steel. On the other hand, compared with solid wood, layered engineered wood has more consistent mechanical properties, can minimize the influence of flaws like knots and get rid of the limitation of dimensions. Yet there is limited number of research focusing on the fracture behavior of layered engineered wood. This article investigates the fracture behavior of the laminated veneer lumber (LVL) via acoustic emission in mode-I fracture tests. The fracture test is conducted on an LVL sample with the help of six AE sensors to collect the AE waveforms. The difference of time of arrival is used to locate the crack tips. Since wood is highly heterogeneous, instead of using the wave velocity for AE source localization, this work leverages the benefits of probabilistic machine learning methods. Moreover, to capture the outliers more easily and being less sensitive to noise, a new type of probabilistic machine learning method, i.e., student-t process regression (STPR), is firstly proposed to address the AE source localization in mode-I fracture tests. As a comparison, the Gaussian process regression (GPR) is also implemented. Because not all AE events are related with the crack tip propagation, the Gaussian mixture models is implemented to identify AE events relevant with the crack tip. The results suggest STPR can outperform GPR both in pencil lead break test and in the mode-I fracture tests, only with the cost of having one additional parameter, i.e., the degree of freedom.
Effect of Piezo Sensor Design on EM Impedance-Based Process Monitoring and Guided Wave Based Structural Health Monitoring for Cryogenic Composite Propellant Tanks
Within this work, different sensor designs were analyzed with respect to EM impedance-based process monitoring and guided wave based structural health monitoring. Therefore, fully coupled piezo-structural harmonic and transient FE simulation are performed on the different sensor designs to assess the change of the EM impedance before and after curing and guided wave propagation and defect interaction in composite plates. The simulation results are experimentally validated by EM impedance measurements before and during curing and the measurement of the response of the bonded sensors during guided wave propagation and defect interaction on composite plates.
Self-Diagnosis of Acoustic Emission Sensors: Electromechanical Impedance-Based Damage Detection
The reliability and functionality of Acoustic Emission (AE) sensors is essential for their role in non-destructive testing (NDT) and structural health monitoring (SHM) systems. Conventional field-verification methods, such as the pencil lead break test (Hsu- Nielsen source), require manual interaction and are susceptible to external influences from the test structure. Furthermore, human interaction in close proximity to the sensor location requires a considerable effort when monitoring structures such as bridges or wind turbines. These shortcomings often necessitate additional verification under laboratory conditions, thereby underscoring the requirement for a more efficient and reliable approach to the verification of AE sensor performance in the field. A promising solution is the analysis of the electromechanical impedance (EMI) spectrum of AE sensors. This method facilitates remote and automated self-diagnosis by capturing the mechanical and electrical characteristics of the sensor, as well as its interactions with the coupling layer and surrounding structure. This paper presents the experimental investigation of the detection of mechanical damage in AE sensors using the EMI spectrum. The damage was induced systematically by controlled drop-down tests, which simulated realistic degradation scenarios. The experimental results are further validated by comparison with an established wavebased verification setup described in the guideline for the verification of sensors and their coupling in laboratories by the German society for non-destructive testing, which is a widely accepted standard for AE sensor verification under laboratory conditions. The comparison of EMI-based self-diagnosis with the wave-based SE02 verification method demonstrates that the EMI is capable of reliably detecting sensor damage. This provides evidence that the EMI-based self-diagnosis is a viable alternative to traditional manual and laboratory-based methods. Moreover, this approach offers several significant advantages, including automation, reduced reliance on human input, and suitability for deployment in field conditions. By addressing common damage scenarios, such as mechanical degradation from impacts, this work contributes to the development of robust self-diagnosis capabilities for AE sensors.
Acoustic Emission-Based Decision Support for Bridge Safety in Smart Cities
This paper presents an acoustic emission (AE) based Identification of Active Anomalies (IAA) system, using signal clustering, image recognition, and machine learning, for monitoring the condition of a highway overpass near a large urban agglomeration. The monitoring results provide the basis for assessing the structural condition of the asset and for the automated load (traffic) control necessary to ensure its safe operation. Acoustic emission (AE) signals recorded during service loads undergo multi-parametric signal analysis using pattern recognition and are assigned classes corresponding to specific anomalies in the material or structure. Each class is associated with a level of structural hazard, ranging from safe operation to loss of safety. Corresponding traffic control measures, including vehicle speed and weight limits, ensure safe operation. The method was experimentally applied to monitor an A2 highway overpass, part of the Łódź city transport hub, facilitating north-south and east-west travel in Poland. This enables highway administration services and agencies to prevent sudden, unforeseen structural failures proactively. The IAA system proves to be an effective diagnostic tool for the efficient and safe operation of a Smart City, enabling the rational allocation of funds for road infrastructure maintenance
Corrosion Identification, Location and Assessment of Prestressing Tendon Anchorages in Suspension/Cable-Stayed Bridges: Integrating Image Recognition and Machine Learning with Acoustic Emission Method
The intensive development of transport infrastructure in Poland and worldwide has led to a rapid increase in the number of engineering bridge structures. Prestressed concrete is currently the most commonly used structural material (43.4%). One advantage of prestressed concrete is the ability to achieve considerable span lengths; consequently, the percentage share of the surface area of such bridge structures is even greater, reaching 58.2% by the end of 2017. Although visual checks are feasible for tendon components, assessing prestressing force and diagnosing cable issues (like corrosion, grout condition, and voids) in prestressed structures is a significant technical and research challenge. This paper introduces a monitoring approach, using periodic inspections or continuous electronic monitoring, to evaluate their condition. This approach employs a novel measurement system that identifies anomalies by recognizing patterns in acoustic emission signals. Furthermore, the pattern classes have also been correlated with crack opening widths, allowing for tracking the influence of crack development on the structural stiffness and monitoring other processes such as corrosion and slippage at the anchorage.
Wall Thickness Loss Estimation Based on Acoustic Emission in Steel Structures
Corrosion is a leading damage mechanisms in the degradation of marine assets. Acoustic emission (AE) monitoring has gained increasing interest as a technique for continuous monitoring of corrosion damage. This study numerically and experimentally investigates the feasibility of wall thickness loss estimation from the AE signals due to localized corrosion. The interaction of the elastic waves emitted due to the evolution of corrosion damage are influenced by the local thickness and material properties of the structure. A steel plate of (500 mm x 500 mm x 10 mm) with a localized wall thickness loss between 0 and 80% in the center of the plate was considered. The numerical investigation was conducted using a higher-order finite element model. Laboratory experiments were performed on a carbon steel specimen instrumented with 7 AE transducers ( 40 - 250 kHz). Corrosion damage was artificially introduced in the steel plate by progressively milling a pit in the center. At different stages of wall thickness loss, simulated AE sources were generated. The response of the structure was evaluated based on signal characteristics such as amplitude, rise-time, frequency content, and waveform. A correlation between the signal amplitudes and the wall thickness loss was observed in both experimental and numerical results. This perspective is promising for the feasibility of corrosion-induced wall thickness loss estimation based on AE measurements.
In Situ Estimation of the Thickness of Oxidized Layer in Composite Structures by Means of Ultrasonic Guided Waves
Under the exposition to air rich in oxygen, oxidation leads to a non-negligible thickness of oxidized layer (TOL) which appears initially on the free surfaces of composite structures. This oxidized layer has modified mechanical properties in comparison with the virgin ones and is progressing during time within the composite thickness as evidence of diffusion-limited oxidation. Being able to estimate in situ the TOL of composite structures under service is thus of great importance for safety and maintenance purposes. Ultrasonic guided waves (UGW) propagating in aged composites will have different velocities in comparison with UGW propagating in virgin composites. Taking benefits of this physical properties linking TOL thickness to UGW velocities, a strategy allowing to estimate in situ the TOL of composite materials is here proposed. As a first step towards its implementation, this strategy is here validated numerically using semi-analytical models allowing to predict UGW velocities in composites as a function of TOL. This model allows to demonstrate the sensitivity of the various UGW propagating modes to TOL thickness for signals representative of SHM applications. As a next step, the proposed approach will be used experimentally for the evaluation and tracking of the in situ growth of TOL. Such a tool can be deployed in situ and help to follow the health state of composite structures in practical applications.
Research on Acoustic Emission Characteristics of Deterioration Reinforced Concrete Beams During Bending Load Failure
In order to investigate the acoustic emission (AE) characteristics of deteriorated reinforced concrete beams during bending load failure, three-point bending test was conducted on four reinforced concrete beams with varying degradation level simulated through freeze-thaw and corrosion cycle. AE parameters of reinforced concrete beams with different degradation level during bending load failure were analyzed, and the influence of deterioration level on AE parameters was revealed. The results show that the maximum cumulative ringing counts and AE energy decrease with degradation degree increasing. High-amplitude signal appears earlier comparing with that of nondeterioration beam. As deterioration degree continues to increase, the proportion of AE events with low rise-time continuously decrease, and proportion of AE events with high rise-time (rise time > 150μs) exhibits a continuously increasing trend. Freeze-thaw and corrosion deterioration significantly reduce the strength and toughness of reinforced concrete beams, which causes significant changes in AE parameters. Therefore, the influence of deterioration should be considered in damage identification and early warning for in-service reinforced concrete bridge by using AE technology.
Hybrid SHM Sensor Network Feeding Multiple SHM Systems for Monitoring Plate-Like Structures Under Cyclic Fatigue: First Results of a Dataset Under Construction
Several Structural Health Monitoring (SHM) technologies and approaches have been developed so far to continuously monitor the condition of a structure. They can be cate- gorized as active and passive systems, giving different insights regarding the monitored specimen when it comes to damage detection. Regardless of the specific system, the goals are to detect, localize, and characterize emerging defects much prior to they can induce catastrophic failure. However, qualifying such a condition monitoring system is paramount to achieve industrial deployment. Nonetheless, assessing reliability of the resulting system remains a challenging task, even when resorting to standard methods such as probability of detection. In general, it is hard to simulate all the influencing factors through laboratory tests and demonstrate whether the qualification procedure can be transferred from specimen to specimen or whether it is strongly system dependent. Having these challenges in mind, this paper presents the first few specimens of a novel fatigue crack dataset under construction recorded from a hybrid sensor network feeding multiple SHM systems based on ultrasonic guided waves, electro-mechanical impedance, acoustic emission, fiber Bragg gratings and any combination thereof. The idea of this data set is to publish it as open access in order to enable people and especially early-career researchers and students to test and explore including aspects like specimen- to-specimen variability, data merging, etc.
Real-Time Structural Damage Detection Using Only Signals Acquired During Impact Events
This work presents a novel, real-time, and baseline-free methodology for structural damage identification during impact events, utilizing the Hilbert-Huang Transform (HHT) to analyse acquired stress wave signals generated by the impact. The proposed technique is based on the decomposition of wave propagation modes induced by both low- and high-velocity impacts, with a focus on distinguishing between extensional (symmetric, high-frequency) and flexural (antisymmetric, low-frequency) components of the AE signals. The empirical mode decomposition (EMD) is applied to extract intrinsic mode functions (IMFs), enabling the separation and reconstruction of the fundamental wave modes. The instantaneous energy content of each mode is then calculated using HHT, and a damage index, Λ, is introduced, a dimensionless quantity that characterizes the shape and spread of the instantaneous energy spectrum obtained via HHT. Experimental validation was carried out on two sets of impact results including flat plates and complex composite structures. Impact scenarios varied from low velocity to hypervelocity regimes, using projectiles of different materials and geometries. The results demonstrate that a Λ value below a threshold corresponds to elastic impacts with no or minor deformation, while values approaching or exceeding the threshold indicate the onset of permanent damage, such as indentation, penetration, or complete perforation. For highly damaged structures, Λ values increased significantly, confirming the dominance of extensional wave components in such cases. This technique shows strong potential for real-time structural health monitoring (SHM) in aerospace and other safety-critical applications, offering high sensitivity to damage without requiring baseline signals. Furthermore, its robustness across different materials and geometries suggests wide applicability for both laboratory and operational environments.
Advances in Electrical Characterization Techniques for Smart Construction Materials in SHM Applications for Masonry Buildings
It has been shown that incorporating an adequate amount of electrically conductive fibers into structural mortars and clay bricks, near the percolation threshold, improves their ability to monitor local load redistribution, leading to damage-sensing capabilities. Moreover, reducing the fiber concentration allows for the detection of piezo properties, which may include piezoresistive, piezocapacitive, or piezoelectric behaviors. This topic is of specific interest in this work, where we examine different electromechanical characterization techniques to study various types of fillers for composite mortars and bricks, including carbon-based materials (carbon nanotubes, microfibers, graphene), steel fibers, and bioinspired structures. The construction materials incorporated in our research consist of cement pastes, mortars, and clay. All those mixes can be classified into percolated and non-percolated materials. The first type of material (percolated) has demonstrated capabilities for damage sensing, while the second material (non-percolated) exhibits a focus on electromechanical properties. In both cases, the key insight is the sensitivity of the construction material. To find out those interesting capabilities, it has been necessary to incorporate different electrical approaches, such as biphasic square, open circuit potentiometry and electrical impedance spectroscopy (EIS). Furthermore, the composition, geometry and number of electrodes, as well as the acquisition system have several implications on the electrical response. Electrical and mechanical measurements must be interpolated based on their respective timestamps, and then correlated and analyzed across both elastic and plastic regions. Additionally, compression and bending mechanisms were applied using ramp and square cyclic loading signals, with frequencies below 5 Hz, depending on the type of characterization performed. Following this logic, we have developed experimental methodologies to perform electromechanical characterizations and constructed models to physically explain the parameters emerging from these electro-mechanical correlations.
Decision Making in Structural Health Monitoring Using Large Language Models
Structural health monitoring (SHM) is fundamental in decision making for damage identification and prescriptive maintenance of civil infrastructure. Traditional decisionmaking methods often fall short in integrating heterogeneous sensor data, inherent to SHM, and the natural language processing required to interpret, e.g., inspection reports, expert assessments, and historical documentation relevant to prescriptive maintenance. To address these limitations, this study introduces a framework for integrating large language models (LLMs) into SHM workflows, facilitating decision making in damage identification and prescriptive maintenance. The proposed framework links convolutional neural networks (CNNs) with generative LLMs. Unlike approaches based solely on prompt engineering, this study applies task-specific fine-tuning via low-rank adaptation (LoRA) to the Mistral-7B-Instruct-v0.1 model, using CNN-generated output and damage metadata as input. The results demonstrate – apart from the proof of concept – a successful generalization to previously unseen CNN-generated output, enabling context-sensitive damage identification. In conclusion, integrating LLMs into SHM workflows allows synthesizing heterogeneous sensor data and natural language, thus enhancing decision making for damage identification and prescriptive maintenance of civil infrastructure.
Carbon Yarns for Strain Sensing of FRC Elements under Cyclic Loading in the Uncracked Regime
The goal of this study is to explore the use of carbon-based textiles to sense strain in fiber-reinforced concrete (FRC) elements. To answer this goal, the study investigates a new electrical configuration in which the carbon yarns function as internal devices within the FRC body. An impedance spectrum analysis is used to monitor changes in both electrical resistance and capacitance, focusing on tensile stresses in the healthy state of the element. The results show that the electrical responses consistently follow the mechanical loading and can be correlated to them for the purpose of strain sensing.
Detecting Cross-Sectional Degradation in TRC Elements by Advanced TDR Technique
This study explores the capability of smart self-sensory carbon-based Textile Reinforced Concrete (TRC) elements to locate and detect cross-sectional degradation. The concept involves connecting carbon yarns into electrical circuits using Time Domain Reflectometry (TDR) technique to measure electrical changes caused by wetting events. To detect cross-sectional degradation, the monitoring approach compares measurements from the current wetting event to those of a healthy reference measurement. The study presents the capability of the new monitoring concept by experimental investigation. It is demonstrated that the smart sensory carbon yarns successfully detect the location and estimate degradation of the cross-section of the elements.
Non-Destructive Plastic Deformation Estimation of Metals Using GMR Sensor-Based Eddy-Current Measurement
This study presents the development of a non-destructive plastic deformation estimation technique for paramagnetic metals, such as aluminum alloy and stainless steel, using Giant-Magneto Resistive (GMR) sensor-based eddy-current measurement. Plastic deformations in metallic structures can occur when the loading exceeding the yield strength of the material is applied. In addition, plastic deformation can induce the initiation and growth of fatigue cracks, leading to the catastrophic failure of metallic structures. Therefore, it is crucial to detect and evaluate the severity of plastic deformation in advance to ensure structural integrity and safety. Plastic deformation in metallic materials causes perturbation (or dislocation) in the material lattice, resulting in changes in electrical resistance (or electrical conductivity). Thus, in this study, the plastic deformation-induced electric resistance variation was estimated using eddy- current measurement. The sensitivity of eddy-current measurement was enhanced by replacing the conventional pick-up (sensing) coil with a GMR sensor. Test specimens using aluminum alloy (Al6061-T6) and stainless steel (SUS304) were fabricated, and various tensile plastic strain levels were applied using a hydraulic loading machine. The phase values were extracted from the eddy-current raw signal, and the relationship with the plastic strain level was investigated. Additionally, the influence of the external magnetic fields was minimized by applying a constant weak magnetic field using a permanent magnet. The experimental results indicate that the phase values of the eddy- current signal increase as the extent of plastic deformation. The uniqueness of this study lies in 1) the fabrication of the eddy-current probe based on the GMR sensor, 2) the minimization of the external magnetic field to enhance the eddy-current measurement, and 3) the experimental validation of the plastic deformation estimation using the eddycurrent phase values.
Non-Destructive Identification of GFRP Rebar Using Spatial Averaging of GPR Radargrams
This study presents the development of a non-destructive Glass Fiber Reinforced Polymer (GFRP) rebar identification technique for reinforced concrete structures based on Ground Penetrating Radar (GPR) B-scan images (radargrams). Conventional steel rebars are vulnerable to corrosion, and the consequential weakening of concrete from the inside can lead to the catastrophic failure of concrete structures. In addition, even when corrosion is detected, the damage is far advanced, leading to costly repairs and maintenance. Recently, GFRP rebars have been gaining interest because of their lightweight, non-corrosive, and high tensile strength characteristics, combating the limitations of conventional steel rebars. GPR is one of the non-destructive inspection techniques for concrete structures using electromagnetic waves. The existence, location, and diameter of steel rebars can be identified using the electromagnetic waves reflected from steel rebars owing to the significant differences in dielectric constants between concrete and steel rebars. However, for GFRP rebars, it becomes challenging because the dielectric constant difference between GFRP rebars and concrete is much less than that of steel rebars. Thus, in this study, the GPR-based GFRP rebar identification technique is developed by averaging multiple radargrams obtained from adjacent line scanning paths (spatial averaging). Concrete specimens were fabricated by inserting actual GFRP rebars with various diameters. Then, the existence and location of the GFRP rebars were estimated using GPR radargrams. The performance of the developed technique was evaluated qualitatively by calculating the standard deviation of the noise component. The results indicate that GFRP rebars are more clearly identified using the developed technique.
Leveraging Fractal Geometries to Improve Structural Health Monitoring Systems
Fractal geometries are characterized by interesting properties that have not yet been exploited for structural health monitoring (SHM). Among such properties, scale invari- ance and self-similarity are worth mentioning. We investigated whether using fractal geometry to develop sensors or self-sensing structures can enhance SHM performance. The latter solution brings about the possibility of integrated damage diagnosis by elimi- nating the need for external sensors. The theory of fractals was used, along with numerical simulations and experimental tests, to evaluate the feasibility of designing sensors and structures with fractal properties for enhanced SHM performance. In particular, a unique solution to develop a sensor or structural component based on the Sierpinski triangle was studied. Due to its hierar- chical structure, the considered fractal geometry inherently possesses frequency domain characteristics that make it highly sensitive to local variations across scales. Preliminary results brought evidence that fractals can be exploited to design highperformance SHM solutions, either as standalone fractal sensors or as self-sensing fractal structures. In addition, the efficient, lightweight, and multiscale nature of these systems makes them suitable for applications in several fields, including aerospace, mechanical, and biomedical engineering. This work contributes to the advancement of SHM technologies by introducing a new paradigm in sensor design and damage detection. The combination of fractals with engineering applications shows the feasibility of advancing engineering by integrating complex physics principles and has potential implications for a wide range of industries.
Activities About Applying Distributed Optical Fiber Sensors to Analyze the Internal Structure of Road Pavements
Structural health monitoring (SHM) using distributed optical fiber sensors (DOFS) is actively conducted in civil engineering and aviation fields. One such activity is establishing road infrastructure that supports mobility services by embedding DOFS in the pavement to capture vibrations from moving entities such as pedestrians and vehicles. Road pavements consist of multiple layers, including asphalt and gravel layers, with different physical properties, such as elastic modulus and density for each layer. While it is easy to evaluate amplitude values and surface temperature using sensors quantitatively, it is challenging to install sensors within the pavement, and few activities allow for quantitative evaluations. On the other hand, DOFS exhibit excellent workability, allowing them to be easily embedded within the internal structure of road pavements, which holds the potential for understanding the internal structure through measurement. Distributed acoustic sensing (DAS) is one measurement technology that uses optical fibers. It can measure the elongation strain applied to the optical fiber over its entire length at a high sampling rate of up to several kHz. Verifying the quantitative nature of DAS is crucial for enhancing the detection accuracy of moving entities. The authors focused on the Falling weight deflectometer (FWD) test, which is commonly used to determine the elastic modulus of road pavements. They comprehensively evaluated the measurement values obtained from the FWD test in conjunction with those from DAS.
Reliable Corrosion Monitoring in RC Structures Using a Temperature-Insensitive GPR Approach
Corrosion of reinforcing bars is a primary cause of deterioration in reinforced concrete structures. Early detection of corrosion is critical for timely maintenance and ensuring structural safety. Ground penetrating radar (GPR) has shown strong potential for corrosion monitoring due to its sensitivity to changes in material properties induced by corrosion. However, conventional GPR systems face challenges in detecting early-stage corrosion and tracking variations in corrosion rate, primarily due to their limited responsiveness to subtle material changes and susceptibility to signal dispersion caused by temperature fluctuations. To address these limitations, we propose a monitoring method based on a perpendicular-polarized antenna system, which enhances electromagnetic interaction with the cross-section of rebars. This configuration enhances sensitivity to subtle corrosion-induced material changes surrounding the rebars. In addition, a temperature-insensitive intersection point, derived from second-order rebar reflections, is introduced as a reliable indicator for tracking corrosion progression. This point amplifies the detection of subtle corrosion-induced changes through multiple signal interactions while maintaining robustness against temperature-induced signal dispersion. Experimental results validate the effectiveness of the proposed method, demonstrating enhanced sensitivity to early-stage corrosion and improved tracking of corrosion rate variations. The approach offers a reliable and robust solution for long-term, non-destructive corrosion monitoring in reinforced concrete structures.
Graphene-Based Sensor for Low Stain Sensing Applications
The innovation and application of advanced functional materials have significantly contributed to the development of sensors for structural health monitoring (SHM)-oriented applications, paving the way for a new era of intelligent structures. Graphene-based sensors have emerged as highly promising owing to their remarkable mechanical, thermal, and electrical properties. Such sensors offer high sensitivity, excellent stretchability, and multifunctional capabilities for detecting various structural parameters. This work explores the development and characterisation of graphene ink films for low-strain sensing applications. Conductive graphene ink films are fabricated using the electrospinning technique. The morphology, electrical properties, and gauge factor of the films are investigated. The effect of strain rate variation on the sensing performance is also investigated. The gauge factor ranges from 10.2 to 11.3 under every condition. Additionally, cyclic bending tests confirm the repeatability and stability of the sensors, demonstrating minimal signal drift and consistent gauge factor performance across loading cycles. The results indicate that graphene ink films offer an easy, power-efficient, weightless and non-intrusive solution for low-strain sensing with potential applications in areas such as SHM and wearable electronics.
Damage Localization and Uncertainty Analysis of Weld Cracks in Portal Steel Frame Based on Vibration and Electromechanical Impedance Technique
Steel structures widely used in modern infrastructure development due to their fast assembly and easy of repair abilities, often rely on welded connections. However, these connections are prone to damage due to corrosion and dynamic loads, leading to progres- sive cracks and sudden structural failure. Hence, these connections should be continu- ously monitored for damage to ensure structural safety and sustainable use. Structural health monitoring methodologies using electromechanical impedance (EMI) have been proven to be useful and reliable techniques for evaluating the state of a structure at early stages of damage. These investigations uses strain gauge for acquiring vibration data, and PZT sensor for getting impedance data through electromechanical coupling. The test frame is a two-story asymmetrical steel portal frame with welded connections at the joints. The connections are subsequently damaged using a manual saw cut to sim- ulate the crack in the welded connection. At first, the strain data has been recorded for healthy and various damage conditions. Damage localization, along with uncertainty quantification, has been implemented using vibration time history obtained from strain sensors. Subsequently, the conductance signature for healthy and different damage states has been monitored. Different statistical damage parameters like modified root mean square deviation (mRMSD) and Average Canberra Distance (ACD), and Discrete wavelet transform based parameters like wavelet coefficient mean, skewness, and kur- tosis are calculated. These damage parameters are then used to formulate an objective function which is optimized using particle swarm optimization (PSO) for damage sever- ity estimation, considering the uncertainty in conductance signatures. The depth of weld damage has been estimated accurately using the proposed methodology, considering the effect of uncertainty. The devised methodology achieved more than 90% accuracy in the severity estimation of the saw cut damage and can be used as a low-cost continuous SHM solution for steel frame structures.
Analyzing Concrete Cracking Along Civil Infrastructure using Distributed Fiber Optic Sensing
Crack monitoring using distributed fiber optic sensing (DFOS) in civil engineering has evolved significantly during recent years. Scientific and commercial applications often focus on distributed strain sensing based on Rayleigh scattering due to its high spatial and strain resolution despite its limited sensing range. Brillouin sensing techniques are more suitable for monitoring large-scale civil infrastructure as they can provide measurements over numerous kilometers. The improved sensing range, however, results in spatial limitations and therefore, restricted suitability for strain-based crack monitoring. This paper presents an enhanced laboratory test series, in which the suitability of various Brillouin interrogators for concrete crack monitoring was evaluated. Five individual concrete specimens equipped with multiple installation setups were investigated under well-known laboratory conditions, where the resulting fiber optic strain sensing data could be related to the true crack width obtained by high-resolution distance transducers over the crack. An alternative method relates alterations in the raw Brillouin frequency spectrum (BFS) to local distortion events like cracks, which is however an extensive, time-consuming process. Artificial intelligence (AI) is therefore applied to the test data to identify BFS anomalies and relate them to the locally arising crack width. First results demonstrate that AI can be an efficient tool to optimize traditional DFOS monitoring strategies, but further optimization is required for reliable determination.
SIMORGH-SHM: A Novel Software for Fully Automated and Precise Fracture Monitoring Based on Acoustic Emission Localization and Moment Tensor Inversion
Passive ultrasonic stress wave, or acoustic Emission (AE), monitoring is a highly effective technique for continuously assessing the structural health of materials, aiding in the prevention of potential failures. AE refers to elastic waves generated during fracture processes, which are detected and recorded by ultrasonic transducers. Quantitative geophysics-based methods enable processing of recorded waveforms to monitor and characterize the spatio-temporal growth of fractures in brittle materials such as concrete and composites. Due to the complexity of the recorded elastic signals and the non-homogeneous nature of the medium, data processing is often performed manually. The high processing costs associated with large datasets, often exceeding terabytes, have limited the practical application of this approach in real-world scenarios. Therefore, an automated methodology is required to reduce costs while maintaining high precision, enabling its integration into Structural Health Monitoring (SHM) and Non-Destructive Evaluation (NDE) frameworks. This paper presents the application of a novel automated and high-precision AE monitoring algorithm and software, SIMORGH-SHM, designed for applications ranging from materials testing to seismicity. The software is compatible with various standard data formats and is capable of processing both trigger-based and continuous data streams. After introducing the software package, initial results from AE monitoring of a 4.2-meter-long Ultra-High-Performance Fiber-Reinforced Cementitious Composite (UHPFRC) T-beam are discussed. The beam was equipped with 24 novel embedded ultrasonic transducers and tested in EPFL’s Structures Laboratory under cyclic loading to failure. Source localizations were performed and damage mechanisms were estimated using Moment Tensor Inversion (MTI) techniques.
A Study of the Temporary Bridge Crossing for Appalachia After Hurricane Helene
Hurricane Helene triggered more than 2,000 landslides and resulted in severe flooding across western North Carolina, which widened stream channels and caused damage to 7,000 stream crossings. Some of the stream channels have widened more than three times the original stream width. In the recovery effort, many temporary bridges (20 ft to 70 ft) were built with steel girders and wood decks. Due to the lack of design guidelines, the qualities of the constructed temporary bridges may vary. In this study, a low-cost, vibration-based bridge monitoring strategy is suggested for these temporary bridges.
Use of Distributed Optical Fibre Sensing for Structural Health Assessment of Reinforced Concrete Beams
Structural health monitoring (SHM) plays a vital role in ensuring the safety and longevity of civil infrastructure, where distributed fibre optic sensors (DOFS) offer high-resolution, full-field strain measurement capabilities. However, DOFS performance can be affected by the formation of micro-cracks in concrete, often resulting in erroneous strain data due to spectral anomalies. This study investigates the reliability of DOFS measurements under progressive loading conditions in a full-scale reinforced concrete (RC) beam. A 4000 × 200 × 400 mm3 RC beam was cast using Grade 25 concrete and reinforced with embedded DOFS along the tension rebar. Additionally, another DOFS was added to the bottom of the concrete after curing to measure strain. The beam was subjected to a four-point bending, with loading applied incrementally up to 40 kN. Strain data were acquired using an OBR 4600 system at 5 kN loading intervals. While consistent strain profiles were observed across mid-span, strain spikes and residual strains were identified on both the rebar and the bottom surface, indicating the formation of micro-cracks. Controlled rebar loading tests confirmed that adhesive bonding had negligible impact on residual strain, supporting the effectiveness of DOFS in detecting internal cracking. The findings highlight the potential of DOFS for early-stage damage detection in concrete structures.
Advanced Computer Vision Techniques for Detecting and Segmenting Structural Visible Seismic Damages Under Varied Testing Conditions
A cutting-edge computer vision method, incorporating an attention mechanism, transformer architecture, and a customized U-Net model, is used for pixel-level multicategory detection of visible seismic damage in reinforced concrete (RC) bridge piers. The damage categories include cracks, spalling, reinforcement exposure, crushing, buckling, and structural failure. A semantic segmentation database is created from experimental photos obtained through cyclic tests, shaking table experiments, and real- time hybrid simulations. To ensure seamless reconstruction, smooth blending techniques such as overlapping and mirror padding are applied to the predicted patch masks. The image database undergoes extensive preprocessing, including lens correction, perspective adjustment, labeling, and damage-type balancing using rotation, flipping, Gamma correction, Hue and Saturation adjustments, and blurring effects. Both sample-level and pixel-level data balancing are achieved through hypergeometric distribution and weighted loss functions, respectively, ensuring the desired probability distribution for each damage category. A hybrid loss function optimizes model performance and metrics like Intersection over Union (IoU) and F1 score track training and validation progress. Atrous convolution is integrated for multi-scale feature extraction, enhancing detection accuracy across varying spatial resolutions. The proposed vision-based approach is validated on unseen (out-of-database) RC bridge pier images, demonstrating high accuracy in detecting multicategory seismic damage. Additionally, crack feature extraction is conducted, measuring total crack length, average and maximum crack widths, and angle, while the location of maximum crack width is also investigated. These findings underscore the promise of automated structural health monitoring and post-earthquake safety assessments, enhancing resilience and enabling rapid decision-making.
Integrating BIM and UAV-Based Photogrammetry for Automated 3D Structure Model Segmentation
The advancement of UAV technology has enabled efficient, non-contact structural health monitoring. Combined with photogrammetry, UAVs can capture high-resolution scans and reconstruct detailed 3D models of infrastructure. However, a key challenge remains in segmenting specific structural components from these models—a process traditionally reliant on time-consuming and error-prone manual labeling. To address this issue, we propose a machine learning-based framework for automated segmentation of 3D point clouds. Our approach uses the complementary strengths of real-world UAVscanned point clouds and synthetic data generated from Building Information Modeling (BIM) to overcome the limitations associated with manual labeling. Validation on a railroad track dataset demonstrated high accuracy in identifying and segmenting major components such as rails and crossties. Moreover, by using smaller-scale datasets supplemented with BIM data, the framework significantly reduced training time while maintaining reasonable segmentation accuracy. This automated approach improves the precision and efficiency of 3D infrastructure model segmentation and advances the integration of UAV and BIM technologies in structural health monitoring and infrastructure management.
Smart Railway Safety: Integrating Deep Learning with Vision Transformers for Obstacle Detection and Track Health Monitoring
Railway safety is critical for preventing accidents caused by obstacles present on the track and infrastructure failures. Unlike, other monitoring systems that often solely focus on either obstacle detection or track condition analysis, limiting their ability to provide a comprehensive risk assessment. This research paper presents a multi-modal deep learning framework where a real-time obstacle detection was performed with the use of the YOLO model. However, detecting an obstacle alone is not sufficient for assessing the collision risk; therefore, a Vision Transformer was incorporated to further refine the detection. Vision Transformers enable a detailed understanding of the obstacle type and characteristics; thereby assisting in increasing the efficiency of impact evaluation. Simultaneously, the condition of the tracks was analyzed using Mask R-CNN alongside YOLO to detect cracks on sleepers and identify loose or broken fasteners. A custom dataset consisting of one-minute track videos, where each frame was converted into images, was used for model evaluation. This multi-modal approach ensures high precision in identifying obstacles as well as the track condition. To evaluate the outputs from obstacle analysis and track monitoring, a fusion model is used to aggregate the outputs, resulting in a risk- weighted decision score. This score assists in deciding the next possible action for the train to take while operating. The proposed system shows higher accuracy, improves the railway monitoring and minimises accident risks, making it suitable for real-time deployment in railway networks.
AI-Driven Railway Maintenance for Fault Identification Through Object Detection and Segmentation
Railway inspection is an important task to ensure the safety and reliability of transportation systems. Regular inspection of key components such as sleepers, fasteners and tracks are essential to maintain the infrastructure and prevent accidents. This paper proposes a deep learning-based framework for automating railway inspection by combining object detection and fault segmentation. The experimentation was performed using the data comprising of track videos, which was further refined using image enhancement techniques where each frame of the video has been used for evaluating the models. The pipeline initiates with YOLO11 being employed in the first phase for detecting railway components due to its superior performance in limited data scenarios, with the second phase utilizing Mask R-CNN to detect and segment areas of damage and corrosion in the corresponding detected components. To benchmark our proposed pipeline, RTDETR was also used with Mask R-CNN and observed that YOLO11 combined with Mask R-CNN outperformed RTDETR in terms of accuracy and efficiency. This work highlights the potential of integrating advanced object detection and segmentation techniques to streamline railway maintenance by automating fault detection from images and video. The models were also evaluated on videos captured at varying speeds.
Railroad Bridge Out of Plane Displacement Estimation Using Camera-Laser-IMU System
Transverse displacement, or lateral out-of-plane movement of a railroad bridge, is an important factor in evaluating railroad bridge condition. Excessive movement in this direction can signal potential issues leading to structural failure or an increased risk of derailment. To ensuring the structural stability and safety of railroad bridges, it is critical to monitor the lateral movement of Railroad Bridge. Traditional displacement measurement methods, are usually contact-method often involve complex installation processes that are costly, time-consuming, and pose safety risks, particularly near active train tracks. To address these challenges, this study introduces a non-contact approach for transverse displacement estimation integrating camera, laser, and inertial measurement unit (IMUs). Cameras provide precise position measurements by detecting visible features, while IMUs capture high-frequency movements that cameras may miss. Additionally, camera data corrects drift and long-term errors in IMU readings, improving accuracy. So, to benefit from the strength of both sensor, this study investigate the use of Extended Kalman Filter (EKF) for data fusion and enhancing the estimation accuracy. The effectiveness of this system was evaluated through controlled laboratory experiment simulating bridge pier movements ranging from 1 mm to 25 mm. The UAV movement is simulated in the laboratory considering random translation and rotational movement that may happen because of hovering motion or UAV flying. The system’s performance was validated against reference measurements from LVDTs and ground-based laser sensors. Results demonstrated that fusing IMU and camera data yielded more accurate displacement estimates than using camera or laser alone, confirming the advantages of sensor fusion in bridge motion tracking. The developed system will eventually be mounted on a UAV to enable remote and non-contact railroad bridge monitoring, improving accessibility to difficult-to-reach locations. This advancement enhances safety, reduces costs, and increases efficiency in railroad bridge inspections.
Concrete Spalling Depth Change Monitoring Using Directional Lighting Images
This study investigates the use of directional lighting images to monitor depth changes in concrete spalling. A neural-network-based photometric stereo method (named NFPS) was applied to images of a surface illuminated from varying angles and directions to generate 3D surface reconstructions. A controlled dataset was created by progressively removing material from a wooden block, representative of concrete, using a CNC machine to simulate increasing levels of spalling severity. Directional lighting images were captured at each stage using a mini-ALICS device, producing three samples: D-0 (undamaged), D-6 (6 mm maximum depth), and D-16 (16 mm maximum depth). In captured images under diffused lighting, damage severity was not visually distinguishable; however, directional lighting enhanced image surface texture and shadows, revealing depth differences. The NFPS method successfully reconstructed the 3D topography of each sample, clearly differentiating between sample spalling depths. From the 3D reconstructions, it was visible that sample D-16 had the most severe spalling depth, despite the spalling surface area from plan view being identical to that of sample D-6. This study’s results demonstrate that directional lighting images, combined with photometric stereo, provide an effective means of monitoring concrete spalling depth changes.
Video-Based Displacement, Velocity and Acceleration Measurement of Structural Elements Using Object-Tracking Algorithms
Civil structures experience forces from external events, operational loads, and environmental conditions that induce vibrations and can lead to deformation that can compromise their integrity and safety. Precise computation of their displacement, velocity and acceleration aids in understanding their dynamic behaviour, detect failures early, optimise design and guarantee long-term durability. Traditional sensor-based approach, need physical contact, offer limited measurement points, are expensive, face accessibility issues, and lack scalability. These drawbacks prompt the need for non-contact, vision-based measurement techniques that offer flexibility, affordability, and comprehensive motion tracking. In this project, we propose a video-based method that utilises OpenCV based object tracking algorithms to measure kinematics such as displacement, velocity and acceleration of structural elements like steel cantilever beam, single degree of freedom pendulum and scaled portal frames. Several tracking algorithms such as Lucas-Kanade optical flow, CSRT (Channel and Spatial Reliability Tracker), MIL (Multiple Instance Learning), and KCF (Kernelized Correlation Filters) were leveraged to extract exact motion data over time by processing vibration videos of these structural elements. The videos are pre-processed to identify the structural element as the region of interest (ROI). Tracker-based approaches (CSRT, MIL, KCF) and Lucas-Kanade optical flow were utilised and displacement, velocity and acceleration were derived. The workflow involves frame extraction, grayscale conversion, and tracking algorithms, where Lucas-Kanade estimates vectors at the pixel level, while feature-based trackers record the trajectory of the beam. Plots of displacement vs time, velocity vs time and acceleration vs time depict vibration patterns and natural frequencies. The Fast Fourier Transforms (FFT) derived from both accelerometer data and video-based measurements are compared to validate the accuracy of the video-based analysis. By combining state-of-the art computer vision methods with structural analysis, this work represents a possible step toward modern, non-invasive methods for evaluating dynamic structural reactions. The comparative use of multiple tracking methods improves the approach’s reliability and applicability, paving the way for broader implementation in engineering processes. This approach can be leveraged to bigger structures. This method can be used in subsequent research to remotely examine the structural health of large-scale structures like buildings and bridges. Accuracy and efficiency can be further increased by incorporating deep learning for improved feature tracking and real-time processing.
Leveraging Denoising Diffusion Probabilistic Models for Generating Synthetic Datasets Pertaining to Condition Evaluation of Physical Assets
This study investigates the problem of data scarcity in the structural health monitoring (SHM), nondestructive evaluation (NDE), and condition evaluation of physical assets by presenting a comparative analysis of denoising diffusion probabilistic models (DDPMs), each trained independently on a structurally distinct dataset. The models are evaluated to assess their image generation performance and their ability to reproduce domain-specific visual characteristics observed in real data. Five distinct datasets were used to train separate DDPMs: 1) surface crack images typical of concrete structures and asphalt pavements (448×448 pixels), 2) their masked versions (448×448 pixels), 3) phased array ultrasonic linear scan images along weld lines in a stainless steel structure (256×256 pixels), 4) two-dimensional (2D) microstructural images of powder- bed fusion laser (PBF-L) additive-manufactured (AM) metals from light optical microscopy (LOM) imaging (patches 256×256 pixels), and 5) ground-penetrating radar (GPR) images (patches 256×256 pixels). The training process for each model was conducted using an NVIDIA H200 graphics processing unit (GPU), requiring approximately six days per dataset, with image generation taking approximately 45 seconds per image. The Fréchet Inception Distance (FID) was used to evaluate the quality of the synthetic images, yielding scores of 13.7 to 34.6 for the datasets, indicating high fidelity and diversity across all cases. By leveraging DDPMs, this research not only overcomes the limitations posed by limited datasets but also provides valuable contributions to advancing AI applications in diverse domains, including structural health monitoring, material characterization, and subsurface imaging.
AI-Powered Digital Twinning and Monitoring of Concrete Dams with Drones and Photogrammetry
Concrete dams are critical infrastructure assets that deteriorate over time, making effective monitoring and inspection essential. Traditional manual inspections are often time-consuming, costly, and risky. In contrast, drones provide a faster, safer, and more reliable solution for structural health monitoring. This research develops a digital twin of the dam using aerial imaging and photogrammetry techniques. Creating a highquality 3D model presents challenges due to variations in lighting conditions, camera motion, and shooting angles, which can result in stitching errors, distortions, and blurriness. To address these issues, a preprocessing pipeline is introduced, including image filtering, outlier removal, mesh smoothing, illumination normalization, and local feature enhancement. For damage detection, a human-AI collaboration approach is employed, using an iterative procedure that reduces annotation efforts and improves detection accuracy through human supervision of small data batches. To map detected damage onto the 3D model, a texture updating method is proposed, which avoids the need for model reconstruction by using damage-segmented images or a virtual camera to recapture imagery. Furthermore, a comprehensive dam inspection platform is developed, featuring a user-friendly application that visualizes the 3D model, analyzes and measures various types of damage, integrates GPS coordinates for spatial context, and generates detailed inspection reports. This robust system provides a practical and effective solution for automated dam condition assessment, documentation, and management.
YOLO-DCD: A YOLO-Based Framework for Dam Damage Change Detection and 3D Model Updating
Dam health monitoring is a critical component of dam management, requiring regular inspection, assessment, and maintenance to ensure structural integrity and operational safety. While 3D models of dams can be constructed given sufficient data, maintaining their up-to-date accuracy is challenging. Constructing an entirely new 3D model is typically resource-intensive due to the need for extensive data acquisition and computational effort. To improve efficiency, it is preferable to update only the regions of the model that exhibit actual changes, as unnecessary updates may introduce additional uncertainty. This paper proposes a novel change detection method, YOLO-DCD (YOLO-based Damage Change Detection), designed to support efficient 3D model updating using a single input image. A real image is first acquired, and its spatial location within the existing 3D model is estimated using a pixel-level localization technique. Based on the established pixel correspondences, the camera pose is computed using the Perspective-n-Point (PnP) algorithm combined with Random Sample Consensus (RANSAC) for robustness. A rendered image is then generated within the 3D model using a refined camera pose, adjusted to ensure complete coverage of the observed region in the real image. Damage detection is subsequently performed on both the real and rendered images using a YOLO(You Only Look Once)-based detection model. The comparison between the two detection results enables the identification of potential damage-related changes, which are categorized as unchanged regions, newly formed cracks, or new spalling. The proposed YOLO-DCD framework is evaluated using the Upriver Dam dataset, with experimental results demonstrating that the method achieves accurate and reliable detection of structural changes, facilitating efficient and targeted 3D model updates.
AI for Automatic Defects Detection on Large Civil Works: Our Adventure from the Database Creation to a First Efficient Pipeline
EDF, as a responsible operator, inspects and maintains the civil engineering structures of its nuclear power plants, which often exceed several tens of thousands of square meters in surface area. These inspections are carried out using drones, generating orthophotos of the surfaces. Manual analysis of these images to identify defects such as cracks, corroded bars, and spalling is costly and time-consuming. To address these issues, EDF is developing an AI-based tool for automated analysis to detect, locate, and measure these defects. The project includes a state-of-the-art review, creation of a training database, image preprocessing, data augmentation, neural network implementation, performance metrics selection, and optimization of the processing chain. The first results obtained and feedback on this image processing pipeline will be presented.
Towards Reliable Crack Growth Monitoring: A Preliminary Study on Fiducial Marker-Based Camera Repositioning Errors
This study explores the precision and practical feasibility of using fiducial markers and projective transformations for non-contact displacement measurement in structural monitoring applications. By tracking a grid of ArUco markers affixed to a planar surface, image alignment was achieved using homography-based rectification, enabling subpixel accuracy in displacement and strain evaluation. A high-resolution industrial camera setup with calibrated optics and image averaging was used to minimize noise. The impact of camera repositioning errors - translational and rotational – on displacement accuracy was systematically evaluated through three controlled scenarios. Results show that while minor displacements (e.g., 5 mm shifts) can introduce errors up to hundreds of micrometres, careful alignment and the use of projective correction significantly reduce this effect. The findings highlight both the promise and the limitations of this low-cost, repeatable approach, especially for applications such as crack monitoring, where high spatial precision is required.
High-Definition 3D Digital Twin for Damage Mapping for Bridge Systems Using Advanced NeRF and STRNet
This research presents an innovative approach to generating three-dimensional (3D) digital twin models for damage assessment in large-scale civil structures. By refining the Nerfacto model, which builds upon Neural Radiance Fields (NeRF), the study achieves highly accurate 3D reconstruction with detailed, pixel-level damage visualization. To enhance precision, STRNet with Test Time Augmentation (TTA) was utilized for pixel-wise crack segmentation, ensuring seamless incorporation of damage data into the digital twin model. Extensive case studies validated the effectiveness of this methodology for large-scale infrastructure, highlighting its potential for proactive structural health monitoring. Future investigations will focus on real-world outdoor applications, integrating UAV-based imaging while addressing challenges such as scale variations and lighting inconsistencies. This framework provides a reliable foundation for automating infrastructure inspection and maintenance.
A Multimodal Fusion Architecture and Dataset: Advancing Camera and Geophone Integration for Smarter Infrastructure
Computer vision has become integral to the operation and management of complex systems, improving functionality by reducing the reliance on physical contact sensors. Multimodal data fusion techniques further enhance these systems by integrating diverse data sources to uncover complex patterns in the environment. While RGB images are often fused with other imagery types (e.g., Lidar, infrared) to enhance system performance under challenging conditions such as poor weather or occlusions, civil infrastructure applications present distinct challenges. These challenges stem from limited robustness under non-ideal conditions, privacy concerns, and resource constraints—factors that demand the integration of heterogeneous data from both vision-based and non-vision-based sensors. Low-dimensional data, such as time-series data collected in situ from physical sensors, can provide valuable information without compromising privacy or overburdening computational infrastructure. However, this data generally contains less information, making it more difficult to train robust models and underscoring the need for a deeper understanding of the tradeoffs among information value, scalability, and the level of privacy offered by different data types. In this paper, we develop a time-synchronized dataset that includes RGB images and geophone vibration data collected in situ. We then evaluate baseline models comparing vehicle classification performance of unimodal models to that of a spatial-attention fusion model for vehicle classification on a noisy urban road. Our preliminary fusion model shows improvements in classification accuracy over the image- or vibration-based models alone, laying the foundation for broader integration of diverse vision and non-vision modalities.
A Visual Question Answering Model to Automate Nondestructive Evaluation Image Analysis
This study introduces a Visual Question Answering (VQA) model designed specifically for nondestructive evaluation (NDE) applications. VQA models allow inspectors to interactively query NDE images—asking targeted questions like "Is there a crack?" or "Where is the defect located?"—and receive precise answers from the model. Leveraging deep learning and natural language processing, the developed system integrates image feature extraction (via a ResNet-50 model) and language generation capabilities (via GPT-2) to provide accurate, informative feedback. By enabling direct question-and-answer interactions, this VQA model significantly improves inspection efficiency, reduces potential errors, and enhances usability in practical field scenarios.
A Masked Autoencoder-Based Novel Deep Learning Framework of Delamination Detection Using Improved HHT-Based Signal Representations
Ensuring the longevity and operational efficiency of composite structures demands highly effective structural health monitoring (SHM) strategies, particularly for detecting delamination, a critical defect that severely compromises structural integrity. Guided ultrasonic wave (GUW) methods have proven particularly adept at identifying the presence, location, and extent of delamination. Despite these advantages, implementing conventional, supervised deep learning (DL) approaches for GUW-based SHM has been constrained by the scarcity of high-quality, labelled training data and the high computational expense of generating representative damage scenarios. To overcome these limitations, this study introduces an unsupervised DL framework for delamination detection that incorporates a masked autoencoder (MAE) architecture. Firstly, one-dimensional time-series signals from GUW experiments are converted into two-dimensional time-frequency representations using an enhanced Hilbert-Huang Transform, which is chosen for its superior time-frequency resolution and computational efficiency over wavelet-based approaches. In the subsequent phase, the MAE framework is trained exclusively on these wavefield representations corresponding to undamaged conditions, allowing it to excel at reconstructing masked segments. The model is then presented with new, unlabeled data that includes both undamaged and damaged signals. If the input data significantly diverges from the patterns learned during training, the model’s reconstruction of masked regions deteriorates, resulting in higher reconstruction error that serves as an indicator of potential damage. A benchmark of an experimental dataset from the open guided waves platform was used to validate this framework. This dataset encompasses carbon fibre-reinforced polymer plates with 28 distinct damage scenarios. The framework identifies delamination damage through root mean squared reconstruction errors and adaptive thresholds and has been found to outperform state-of-the-art DL architectures, including conventional autoencoders.
A Wireless Enabled Nondestructive Damage Tolerant Inspection Fatigue Sensor for Structural Health Monitoring and Prediction of Remaining Useful Fatigue Life of Aircraft Structures
In this paper, a wireless enabled nondestructive evaluation (NDE) fatigue damage sensor for the structural fatigue health monitoring and prediction fatigue life of structural members of large engineering structures including spacecraft and aircraft structures is presented. The fatigue damage sensor has special designed smart sacrificial sensor beams for early fatigue damage detection and prediction. The smart predictive sensor can measure the state of damage levels and predict the remaining useful service life of structural components or locations for the fatigue life cycle management of structures. The wireless enabled nondestructive evaluation (NDE) fatigue damage sensor consists of mechanical and electronic units. The mechanical part of the fatigue damage sensor has special designed smart and predictive sacrificial beams with different levels of fatigue lifetimes normalized to the total lifetime of a real structure. The smart fatigue damage sensors attached onto the surface on a real structure is expected to have the same cyclic stress-strain loading behavior and the same cyclic stress-strain history for during the service life. The fatigue damage sensor having multiple parallel oriented mini or micro nondestructive fatigue damage measurement beams are designed to fail earlier than a real structure in the different predetermined fatigue lifetimes acting like a fatigue fusing system. The mechanical sensor beams designed with a special geometry and strain magnifying factors to break in the different progressive life intervals compared to the total service lifetime of a real structure. The electronic unit of the sensor can detect the fatigue failures of mini sensing beams and transmit the information wirelessly. The nondestructive IOT fatigue damage sensor directly measures the fatigue damage accumulation levels and the remaining useful lifetime of fatigue sensitive metallic and additively manufactured structural or mechanical members through the special designed fatigue damage measurement beams from critical points of the structure without using any algorithm. The smart structures instrumented with NDE fatigue damage for structural health monitoring (SHM) can provide real-time critical information on the status of the structure. Due to the nondestructive direct fatigue damage measurement nature of the sacrificial-breakable mini or micro electromechanical system (MEMS) sensing beams, the sensor provides real operational and progressive statistical fatigue damage data. The collected fatigue sensor data from the sensor network is quantitative and easy to interpret and understand without any further mathematical analysis. Since the wireless enabled fatigue damage sensor network through Internet of Things (IoT) technologies collect the real operational fatigue data remotely, the statistical fatigue damage sensor network data can be used for the prediction of lifetimes of structural members by using Machine Learning(ML) Algorithms such as Regression, ANN, RF.
Kolmogorov-Arnold Network-Aided Design of Conch Shell-Inspired Acoustic Metamaterial for Rail Noise Mitigation
Urban transportation noise has become a critical challenge for modern cities striving to maintain an acceptable acoustic environment and ensure public well- being. Traditional noise mitigation approaches, such as bulky barriers and thick soundproofing panels, often struggle to balance broadband and high-efficiency sound absorption with lightweight, thin-layer configurations. Acoustic metamaterials (AMMs) offer a promising alternative, enabling high-efficiency sound absorption across broad frequency ranges with reduced weight and thickness. In addition, incorporating biomimicry into the design of AMMs allows us to draw inspiration from the natural structures like conch shells with remarkable sound-dampening properties due to their unique geometries and material compositions. By mimicking these natural designs, we can create AMMs that not only enhance sound absorption but also maintain lightweight and compact configurations. However, the bio-mimic AMM design is often complex, computationally expensive, and requires intricate geometric layouts and multi- parameter optimization. In this study, we propose a novel design framework to address the design challenges associated with conch shell-inspired AMMs by combining machine learning and optimization algorithms. Our framework uses Kolmogorov-Arnold Networks (KANs) to model and predict the acoustic absorption performance based on various geometric and material parameters, specifically targeting the 500–2000 Hz frequency range, which encompasses many of the most disruptive frequencies in rail transport. Particle swarm optimization (PSO) then guides the design towards configurations that achieve average absorption coefficients above 0.80, while minimizing simulation complexity and computational cost. Compared to conventional noise control solutions, this framework significantly reduces volume and complexity without compromising acoustic absorption, making it more viable and cost-effective for train noise abatement. Moreover, by shortening design cycles and reducing iteration costs, our approach can accelerate the development of next-generation noise mitigation acoustic materials, holding a significant promise for creating quieter, more sustainable, and more efficient rail systems.
Enhancing Structural Health Monitoring in Additive Manufacturing Through Embedded Sensors, Infill Designs and Deep Learning
Additive manufacturing (AM) enables the integration of sensors into complex structures. Embedding sensors within the structure reduces cost, allows precise placement, and protects the sensors from environmental exposure. This study investigates the performance of embedded piezoelectric transducers (PZTs) within polymer plates fabricated via AM, using varied infill patterns to influence surface wave behavior. The Surface Response to Excitation (SuRE) method was employed to excite the structure using multiple pulse width excitation (MPWE) and to monitor the resulting surface wave propagation. Signals captured from the embedded sensors were processed using the Short-Time Fourier Transform (STFT) to generate time-frequency spectrograms, which were then classified using Convolutional Neural Networks (CNNs). This approach enabled accurate estimation of both the location and magnitude of applied loads, achieving classification accuracy above 90%. The results demonstrate the effectiveness of combining embedded sensing, infill-based wave manipulation, and deep learning for structural health monitoring. This method shows strong potential for applications in biomedical, aerospace, and mechanical engineering, particularly where polymer components are used in critical functions.
Distributed Temperature Control for Safer Electric Vehicles
As the adoption of electric vehicles continues to accelerate, managing the risks as- sociated with battery technology has become critical. To ensure the safety of people and goods, it is essential to secure battery packs throughout their whole lifecycle, during charging, discharging, logistic and storage phases. However, current battery packs, which must constantly evolve to meet demands for reduced weight, volume, and cost in a competitive market, often underestimate an essential parameter: temperature reading. Temperature monitoring is a key element of battery health, yet it is typically performed at only a few points on the pack, limiting the reliability of the diagnostic. The present study proposes a novel approach based on time-domain reflectometry (TDR) to achieve real-time, distributed temperature monitoring along a single cable sensor. While optical fibbers approaches are discussed in the literature, TDR methods present easier integration, cost-efficiency, robustness, and can be installed with existing battery cables. The possibility of using readily available components such as coaxial cables and time-domain reflectometers with advanced signal processing techniques enables accurate temperature measurement. The first results comprising calibration experiments for various coaxial cable and validated temperature measurements over a range of zero to five meters. Us- ing high-frequency signals, hot spots were detected with a precision of three centimeters, which is ideal for identifying overheating cells in a battery pack. Furthermore, the sys- tem can operate across a broad temperature range, including sub-zero environments and up to a hundred degrees, which is the range for battery surveillance. This innovative solution presents a promising path forward in Structural Health Monitoring, paving the way for safer and more reliable electric vehicle batteries.
Self- and Domain-Awareness for Machine Learning in NDE and SHM
Given any input, a machine learning (ML) algorithm will produce an output, but the confidence and accuracy of that output are currently not well understood or quantified. This poses serious risks in the case of automated safety-critical nondestructive evaluation (NDE) or structural health monitoring (SHM) applications. This work aims to provide a comprehensive approach for quantifying the uncertainty of ML models when used for inspection purposes (self-awareness), in addition to equipping these models with domain-awareness features (i.e., the ability to detect out-of-distribution or “bad” input data). An ensemble of models is trained as part of the procedure, and the reliability of the predictions is measured using the statistics of the models. This information is also used to assess the quality of the input data and evaluate whether it comes from an out-of-distribution source or falls within the domain that the models were trained on. However, this process encompasses making a good choice of the ML model’s type and complexity to ensure model effectiveness and stability. It also involves understanding the trade-off between the ensemble size (number of models within the ensemble) and the prediction’s uncertainty and statistical significance. A robust framework for the whole process is provided and discussed in this paper. The methodology is demonstrated using simulated ultrasound data for corrosion-profile assessment. The presented findings are a step forward towards having more confidence in using ML for NDE and SHM applications. Some current shortcomings are discussed and suggested for future investigation.
Explainable Boosting Machine for Structural Health Assessment: An Interpretable Approach to Data-Driven Structural Assessment
Machine learning models used in structural health monitoring often act as "black boxes," offering predictions without justifying their logic. This lack of transparency undermines trust in safety-critical infrastructure assessments. To solve this, we propose the Explainable Boosting Machine, an interpretable method that explicitly links input variables (e.g., sensor data, and structural parameters) to predictions, enabling engineers to validate results against engineering principles. Real-world structural health monitoring and assessment struggles with sparse data, structural complexity, and hidden biases. Explainable Boosting Machine addresses these challenges by prioritizing transparency and physically meaningful insights. We apply it to predict the shear load- carrying capacity as a percentage of the ultimate load, based on the maximum diagonal crack widths observed on the surface of reinforced concrete beams—a critical metric for shear failure risk. Our results show that the model achieves an RMSE of 10.40% on the test dataset while identifying the influence of key predictors (e.g., beam depth, shear and skin reinforcement ratios). For instance, the model reveals that, for the same maximum diagonal crack width observed in two beams, a structure with a larger depth is farther from failure compared to the one with a smaller depth, enabling engineers to audit model logic and enhance structural assessment. This work advances trustworthy AI in structural health monitoring by bridging data-driven innovation and engineering accountability. Interpretability of explainable boosting machine ensures models remain consistent with physical laws, actionable for decision-making, and adaptable to realworld constraints. We advocate for machine learning frameworks that prioritize transparency as rigorously as predictive performance.
AI-Driven Void Detection Using SPECFEM3D Wave Propagation Simulations
This study introduces a deep learning framework for detecting underground cavities using synthetic seismic displacement data generated by SPECFEM3D. Traditional inversion methods are computationally intensive and often limited in resolution, motivating the use of a 3D convolutional neural network (3DCNN) to learn spatial-temporal features from sparse surface sensor data. The model was trained to predict a continuous 3D probability matrix indicating cavity presence, which was later post-processed for binary classification and evaluation. High accuracy was achieved across regression and classification metrics. Comparing simulated wave responses from predicted cavities to the original sensor data, confirmed physical consistency. Additional blind tests demonstrated robustness under varying conditions. Overall, this approach offers a promising, efficient alternative for subsurface anomaly detection with strong predictive performance and physical reliability.
Image-Based Structural Health Monitoring Methodology for Corrosion Diagnosis and Prognosis
Corrosion is a serious concern for structural integrity since it can lead to premature structural failures. Nevertheless, its monitoring is particularly challenging due to inherent features of the corrosion process: it is strongly dependent on environmental conditions, materials used, and their coupling. Generalised corrosion phenomena are visible at visual inspection and lead to a reduction of the resistant thickness of components and structures. Instead, localised corrosion results in visually difficult-to-detect damages that can lead to fatigue cracks. In addition, these two corrosion types are not mutually exclusive: a component may present corrosion pits even if it is macroscopically subjected to generalised corrosion. However, a probability-based relationship between generalised corrosion evolution and corrosion pits’ presence and geometrical features is missing. Therefore, it is important to monitor generalised corrosion phenomena to assess the structural integrity of structures and components given the reduction in their resistant section and the possible presence of corrosion pits. Within this context, this study presents a methodology for corrosion-based structural assessment by integrating image-based corrosion diagnosis, measurements of environmental and corrosion rate related parameters, and a filtering technique to perform corrosion diagnosis and prognosis. The image-based corrosion diagnosis relies on a Convolutional Neural Network (CNN) that has been trained to automatically perform semantic segmentation on images of a corroded helicopter component. In this way, it is possible to have discrete-in-time observations of the actual corrosion level of the component when the helicopter is not on missions. The implemented CNN is not only able to distinguish between corroded and uncorroded regions but also between two different corroded regions of interest due to different materials. The CNN was trained with manually segmented images from which corrosion indexes have been extracted. The latter have been related to (i) environmental parameters and (ii) corrosion rate related parameters. In this way, two models have been obtained offline to predict corrosion evolution. Eventually, a Particle Filter (PF) was implemented to adapt the models to the observations of the corrosion level by the CNN. The proposed framework integrates image processing, sensor measurements, and filtering techniques for structural assessment. The PF guarantees the adaptability to real-time observations of the corrosion evolution models developed offline, improving the reliability of the methodology. While the presented framework has been applied to a helicopter component, it can be easily applied to other systems. For instance, being based on image processing, its image-based nature makes it well-suited for monitoring offshore or hardto- access structures using drones or fixed cameras. Eventually, this study provides a step towards a more accurate and data-driven corrosion prognosis, enhancing the accuracy of structural integrity assessments.
Ultrasonic Imaging of Crack Detection in Shafts Using Convolutional Neural Networks and Grid-Wise Classification
We present a data-driven convolutional neural network (CNN) framework for detecting internal cracks in a two-dimensional shaft cross-section using transient elastodynamic wave responses. The model is trained using a grid-wise classification approach, where each element in the domain is labeled as damaged or undamaged. Training data are generated through wave propagation simulations in ANSYS Mechanical, where elliptical cracks of varying sizes, orientations, and positions are randomly introduced. Measured wave signals at the boundary are used as input features, and corresponding damage labels for each grid element of unstructured background mesh are used as output. The CNN learns to predict the probability of damage for each grid element based on wave responses from multiple sensors. The trained model effectively reconstructs internal cracks without prior knowledge of their characteristics. Numerical results on out-of-distribution datasets show that the model can reliably detect cracks in various configurations, indicating its potential for ultrasonic structural health monitoring of complex engineering structures.
Unsupervised Damage Diagnostics with Data Normalization Using Experimental Data
Environmental temperature variations can significantly affect modal parameters, complicating damage detection in vibration-based structural health monitoring (SHM) approaches. To mitigate these challenges, this study proposes a novel approach to transform temperature-variant data into a temperature-invariant domain for more effective data normalization and thus more accurate damage detection when the labeled data under temperature variation is unavailable. Experimental acceleration data collected from different structural health states are first categorized into temperature-variant and temperature-invariant domains, and modal parameters (modal eigenfrequencies, eigenforms, and damping ratios) are extracted accordingly. An adversarial domain adaptation technique is then employed to enhance the algorithm’s ability to generalize across domains while accurately distinguishing between damage states. The proposed approach is validated on a real field engineering structure realized as a 9 m high lattice mast under real environmental conditions. The approach consists of three main components: a general feature extractor, a classifier, and a domain discerner. The modal parameters are processed through the feature extractor to obtain domain-independent features, which are subsequently mapped to the corresponding damage states. Meanwhile, the domain discerner competes with the feature extractor by attempting to identify the domain origin of the features, ensuring robust feature extraction. The results demonstrate the potential of this approach to overcome temperature-induced challenges in SHM and improve damage detection accuracy.
Low-Complexity Approach to Intelligent SHM by Combining Machine Learning Models Using Single-Sensor Data
Structural Health Monitoring (SHM) systems, when applied to large civil engineering structures such as bridges, process high-volume data and run computationally intensive algorithms, which typically require important processing power to ensure low inference latency to enable real-time monitoring and rapid decision-making. In this study, we propose a novel resource-efficient approach to optimizing SHM for civil structures. The methodology integrates lightweight machine learning models that rely exclusively on single-sensor data, enabling deployment at the sensor level (smart sensors). By aggregating outputs from multiple sensors, the approach captures spatial information, introduces diversity, and benefits from an averaging effect, significantly improving overall performance compared to individual sensor-based predictions. This single-sensor strategy ensures low computational complexity while maintaining high accuracy, making it particularly suitable for resource-constrained environments. To evaluate the effectiveness of the proposed methodology, we applied it to the Z24 benchmark dataset, a widely recognized SHM resource for civil structures. The objective was to classify various damage scenarios based on data collected from accelerometers deployed on the bridge. The results demonstrate competitive performance with minimal computational complexity, highlighting the scalability and suitability of such an approach for large-scale SHM applications. Ultimately, this study underscores the potential of resource-efficient SHM solutions, contributing to developing sustainable and intelligent monitoring systems.
Transforming Sparse Piezoelectric Measurements into High-Resolution Damage Maps for Structural Health Monitoring
Structural health monitoring of plate-like structures often relies on guided wave propagation to detect and locate damage. While full-field vibrometric measurements offer exceptional resolution and detail, their high cost and complexity make them unsuitable for widespread use in practical applications. In contrast, piezoceramic transducers provide a cost-effective alternative but are limited to collecting data at discrete points, leading to significant challenges in achieving high-resolution damage visualization. This paper addresses this gap by introducing a novel method to transform sparse piezoelectric measurements into high-resolution visual damage maps. This paper presents a novel, image-processing-based method to reconstruct highresolution damage maps from sparse piezoelectric measurements. The approach leverages emitter-receiver pairs to draw color-coded propagation paths, where intensity reflects the severity of damage detected along the wave trajectory. Unlike existing methods such as RAPID, the proposed framework is computationally lightweight, baselineagnostic in terms of damage indices, and compatible with a wide range of signal features beyond Pearson correlation. The proposed method utilizes an image-processing-based algorithm to reconstruct dense feature maps of damage presence. The approach leverages emitter-receiver pairs to draw color-coded propagation paths, where intensity reflects the severity of damage detected along the wave trajectory. The resulting visualizations closely mimic those obtained from full-field vibrometry, while significantly reducing data requirements and computational overhead. To validate the method, experiments were conducted on an aluminum plate equipped with 8 signal transmitters and a laser vibrometer to measure wave propagation. Seven damage scenarios were tested, ranging from barely visible damage to a 17 mm flatbottom hole. This proof-of-concept offers a practical and scalable solution for highresolution SHM and serves as a foundation for more detailed quantitative studies presented in future work.
TDA-Informed Feedforward Recurrent Neural Networks for High-Rate State Estimation Application
Real-time state estimation is essential for conducting efficient structural health assessment and enabling feedback control strategies in high-rate systems. High-rate systems are dynamic systems that experience extreme acceleration (> 100 gn where gn is the gravitational constant near the earth’s surface) within very short periods (< 1 millisecond), commonly observed in applications such as hypersonic systems and impact mitigation mechanisms. These engineering systems require control and feedback methodologies capable of operating within the sub-millisecond ranges, posing significant challenges for traditional prediction methods due to the underlying nonlinear and nonstationary behavior. This paper introduces and investigates a deep learning algorithm that combines topological data analysis (TDA) features with machine learning to enhance state estimation of high-rate systems. The algorithm consists of an ensemble of recurrent neural networks (RNNs) constructed with a parallel arrangement of long short-term memory cells. The ensemble incorporates predictions from multiple RNNs trained on varying window sizes and delays, allowing the system to capture both shortand long-term data dependencies. A Feedforward Neural Network is employed to predict and analyze the contribution of each RNN’s output to an overall state estimation using TDA-derived features such as the maximum persistence of the first dimensional persistence homology group, H1. The proposed approach is validated using data from the dynamic reproduction of projectiles in ballistic environments for advanced research (DROPBEAR) testbed. Results demonstrate the promise of the algorithm at predicting time series and system states over longer prediction horizons.
Multi-Step State Forecasting with Conformal Prediction for High-Rate Dynamic Systems
High-rate systems, such as hypersonic vehicles and impact mitigation mechanisms, exhibit rapid, nonlinear, and rapid time-varying dynamics. These systems are often subjected to extreme accelerations (e.g., > 100 gn) over very short durations (e.g., < 100 ms), making them highly susceptible to uncertainties, non-stationarities, and external disturbances. In such environments, accurate and reliable state forecasting is essential for real-time decision-making and control. This paper presents a novel framework for high-rate multi-step forecasting that integrates recurrent neural networks (RNNs), topological data analysis (TDA), and conformal prediction for uncertainty quantification. RNNs are used to model temporal dependencies in high-dimensional system dynamics, while TDA-derived features capture the underlying topological structure of the system state. To provide robust and interpretable uncertainty estimates, we employ conformal prediction techniques—particularly inductive and block-based conformal methods— to construct statistically valid prediction intervals. These methods adapt to the non-exchangeable nature of time-series data, enabling coverage guarantees under temporal dependence. The effectiveness of the proposed framework is evaluated on experimental data from the Dynamic Reproduction of Projectiles in Ballistic Environments for Advanced Research (DROPBEAR) testbed. Results demonstrate that the model produces accurate multi-step forecasts with well-calibrated uncertainty bounds, which can be used to support high-rate system monitoring and decision-making.
Reconstruction of 2D Guided Wave Propagation Data Using Generative Adversarial Networks
Guided wave propagation analysis is one of the critical areas in Structural Health Monitoring (SHM) that enables the detection and assessment of damage in critical infrastructure. Visualization of wave interactions with structural anomalies, such as holes or cracks, gives important insights into the integrity and behavior of materials. In particular, automatic decision systems taking guided wave data as input can be used to monitor vital infrastructure, provided that they are trained on large amounts of diverse data. Unfortunately, due to the cost, complexity, and time-consuming nature of experimental measurements, obtaining high-quality labeled data for guided wave propagation analysis remains a significant challenge. These restrictions on generating diverse datasets often make it more difficult to train reliable machine learning models for SHM, especially when handling a variety of complex damage scenarios. Generative Adversarial Network (GAN) can offer a solution to those issues by producing realistic synthetic datasets that replicates key features of experimentally obtain data, allowing for cost-effective augmentation of SHM datasets. Those generative models are well suited for this task, based on their ability to simulate accurate representations of guided wave propagation through damaged structures, capturing subtle interactions between waves and structural anomalies. This allows for augmentation of SHM datasets at a reasonable cost, which eventually improves machine learning models ability to generalize damage scenarios that haven’t been seen and reducing dependency on expensive experimental setups. In this paper we demonstrate how to recreate two-dimensional (2D) guided wave propagation data through the aluminum plate with damages modeled as flat-bottom holes. Our method produces high-fidelity images with very close resemblance to training data obtained using laser vibrometer. This approach highlights the potential of generative models to improve data diversity and availability for SHM applications, in particular in simulation of previously unseen damage cases which enable directed augmentation of existing SHM databases. This strategy can reduce the gap between limited experimental datasets and machine learning-driven SHM systems.
Compressed Dynamic Mode Decomposition for Full-Field Modal Identification from Video Measurements
Traditional sensor-based modal identification methods can be intrusive, costly, and limited by sensor numbers and placement. This research presents an end-to-end vision- based automated full-field modal identification framework that overcomes these limitations by combining a Phase-Based Optical Flow (PBOF) method and a Hankel Compressed Dynamic Mode Decomposition (HCDMD) approach. The proposed HCDMD approach automates the full-field modal identification process with high efficiency by employing a compressed sensing strategy to accelerate computation and a Local Standard Deviation (LSD) index to separate structural and noise mode shapes. A laboratory experiment was utilized to validate the effectiveness and efficiency of the proposed framework in measuring full-field displacement and extracting modal parameters. The results demonstrate the potential of this fully automated end-to-end solution for full-field modal identification from video measurements.
In-Situ Ultrasonic Monitoring for Process Monitoring of Digital Light Process 3D Printing
Digital Light Processing (DLP) 3D printing is a widely used vat photopolymerization (VPP) technique known for its ability to produce intricate geometries with a broad range of mechanical properties. However, ensuring consistent, defect-free parts remains challenging, primarily due to shrinkage, partial curing, and uneven exposure. In this work, an in-situ ultrasonic monitoring system is introduced which continually assesses each newly formed layer in real-time. By analyzing how ultrasonic waves travel through each layer, internal flaws down to 0.249 mm² were reliably identified, and dimensional accuracy of approximately 0.2 mm was achieved. Identifying defects precisely when they emerge allows immediate corrective actions, significantly reducing material waste and post-processing efforts. This approach provides a promising solution for enhanced reliability and efficiency of DLP manufacturing, particularly in fields such as biomedical engineering and microfluidics, where precision and material integrity are critical.
Voice-Enabled Structural Health Monitoring via Large Language Models—A Cantilever Beam Case Study
Structural Health Monitoring (SHM) is critical in ensuring the safety and longevity of infrastructure such as bridges, pipelines, and buildings. Traditional SHM systems rely on sensor networks to capture vibrational and visual data, which are then analyzed using signal processing, statistical models, and machine learning to detect damage and predict failures. However, these systems often require expert interpretation and lack intuitive interfaces for real-time insights. Recent advancements in artificial intelligence, particularly Large Language Models (LLMs), present new opportunities to enhance SHM workflows through natural language understanding and data-driven reasoning. In this paper, we introduce a novel LLM-powered voice agent that enables users to interact conversationally with structural sensor data. By integrating LLMs with time-series SHM data, our system can respond to voice queries, summarize experiment outcomes, detect anomalies, and generate visualizations on demand. We validate our approach using experimental data from a cantilever beam subjected to multiple damage scenarios. These results highlight the potential of LLM-driven conversational interfaces to make SHM systems more accessible, interpretable, and actionable for both experts and non-specialists.
AI-Driven Automation in the Czochralski Process: A Step Towards Sustainable Silicon Manufacturing
The transition to renewable energy relies heavily on high-quality monocrystalline silicon for photovoltaics, yet its production via the Czochralski (Cz) process remains resource-intensive and yield-limited. Structural defects during crystallization account for significant material waste, challenging the sustainability and cost-efficiency of silicon manufacturing. The Cz process is the only industrially relevant method for producing monocrystalline silicon, enabling the growth of large single crystals in defined orientations [1]. Despite its technological maturity, increasing productivity while reducing crystallization costs remains a major challenge. The primary yield- limiting factor is the loss of the monocrystalline structure, triggered by various still poorly understood phenomena [2]. Visual monitoring by human operators should be replaced by automated image recognition systems to detect disturbances early and enable timely countermeasures. Logging these anomalies enables subsequent process analysis. Our goal is to integrate process monitoring and analysis into a unified tool that supports continuous evaluation and optimization through machine learning. This work presents a modular and extensible software framework for the automated preprocessing, analysis, and training of machine learning models using real-world manufacturing data. The framework integrates domain expertise with data- driven approaches to support anomaly detection in complex production processes. A central focus is placed on autoencoders [3], which are used to identify deviations in process parameter trajectories, providing a promising tool for quality assurance. The developed Python modules enable flexible handling and visualization of time-series data and serve as a foundation for further research and industrial deployment. While the current implementation does not yet support fully automated anomaly detection in production environments, it offers valuable insights into the practical application of unsupervised learning in manufacturing.
Vibration-Controlled UAV Tap Testing for Predictive Maintenance Using Machine Learning
Early detection of mechanical deterioration in critical infrastructure, such as tall buildings with exterior facades, is essential to optimize predictive maintenance strate- gies, reduce operating costs and minimize structural risks. However, conventional inspection methods have significant limitations, including low robustness to environmen- tal disturbances and a high degree of subjectivity that compromises the accuracy of the results. This study proposes an automated tap testing system for acoustic data collection and analysis, specifically designed to simulate the vibrational conditions associated with UAV flight during actual inspections. The approach combines controlled tap test- ing with acoustic detection, acquisition of acoustic signals on specimens with controlled states (healthy/unhealthy) and systematic introduction of vibrations in three amplitudes (1°, 3°, 5°) to evaluate the robustness of the system. Data processing employs Principal Component Analysis (PCA) for dimensional reduction and discriminative feature ex- traction, followed by clustering techniques, k-means, for automatic state classification. Experimental results demonstrate an effective defect discrimination capability, although a progressive degradation of performance proportional to vibration intensity is observed. This work provides a methodological framework for nondestructive inspections under dynamic conditions, laying the groundwork for future improvements in model general- ization and adaptability to complex environments.
Optimized Deep Siamese Network for Structural Health Monitoring Using Vibration-Based Measurements
Structural health monitoring (SHM) is essential for evaluating infrastructure conditions and ensuring its safety. Over time, the structural integrity of buildings and civil infrastructure can deteriorate, leading to a decline in performance and reliability. SHM has increasingly used deep learning (DL) models for effective damage detection. However, for onsite damage detection of smart structures, there is a need to deploy these trained DL models on resource-constrained devices such as Raspberry Pi, Arduino, mobile devices, or microcontrollers. To accomplish this task, the trained DL models need to be optimized for deployment that enables efficient and timely damage detection in realworld applications by testing the vibration (acceleration) measurements coming from the sensors placed over the structures. As per the knowledge of the authors, this is a crucial aspect that has yet to be fully explored in SHM. In the present work, vibration signals are initially transformed into a 2D matrix format. These transformed 2D matrices are used as input to a DL model, specifically the deep-siamese network (DSN). The DSN operates based on similarity measures between pairs of training samples. This enables the proposed SHM approach to detect and report previously unseen damage scenarios during testing. To enable real-time inference, the DSN model is optimized using pruning, quantization, and knowledge distillation. These techniques reduce the size of the model and the inference time, making it suitable for deployment on resource-constrained devices. To validate this approach, the IASC-ASCE benchmark structure with varying damage conditions is used. The results show that the optimized model maintains high detection accuracy while enabling fast and efficient decision making. Each optimization technique is used to convert the DSN model into a lightweight (.tflite) format. The model is then deployed on a virtual machine running Raspberry Pi OS with Debian Linux. These optimizations support real-time structural health monitoring with high detection accuracy. The model also handles unseen damage during testing, making it suitable for real-world deployment. This makes it a promising solution for real-world SHM, bridging the gap between DL advancements and practical online monitoring.
Unsupervised Fault Detection in Sensor Networks for Extraterrestrial Habitats
Various sensors, such as accelerometers, temperature sensors, and pressure sensors, are employed in deep space habitats to monitor operating conditions. The health management system (HMS) depends on accurate sensor data to make reliable decisions. However, the harsh environment of space, characterized by micrometeorite impacts, fire, radiation, dust, and other extreme conditions, can lead to sensor malfunctions. Consequently, the collected data may be corrupted by anomalies, significantly impairing the performance of the HMS. Detecting sensor anomalies and excluding faulty data from decision-making processes is therefore essential. Unsupervised learning has been widely adopted for anomaly detection, as it can train models using unlabeled datasets. This paper presents a novel unsupervised learning approach based on convolutional autoencoders (CAEs) to detect faults in temperature and pressure sensors within the habitat. The proposed method is thoroughly evaluated using a habitat simulator (HabSim), and its capabilities and limitations are discussed.
Forming an Advanced Proper Orthogonal Decomposition-Based Data Science Framework for Advanced Diagnostics of Practical Multi-Body Systems: SHM Challenges in Complex Structures-and-Flexible Machinery
In an attempt to tackle science-challenging SHM issues in complex multi-body structural and machinery systems, we are developing a methodology that exploits the principles of continuum mechanics (theoretical-computational-experimental) along with optimum reduction analysis to lay data science aspects for data-driven mechanics. The focus is on damage diagnostics in the presence of structural complexity: domain geometry and nonlinearity distribution. Simultaneous ensembles of accelerations form basic datasets to be augmented properly. We introduce two fundamental augmentations of a dataset. The first, referred to as the Simultaneous Three-Point Observation (S3PO), is the augmentation of a dataset with another two similar datasets. The second, referred to as the Spatial Differential Form (SDF), is the augmentation of a dataset with its spatial differential forms of order-1,2. The APOD resolution interpretation of the S3PO datasets of composite beam structures reveals coherence among the individual datasets forming the augmentation: they intersect along modal physics-carrying POD modes. The diametrically opposite is revealed for the rotor structure, modified with holes: the datasets do not intersect at all. This is clearly due to the fact that the dynamics are infested with a great degree of uncertainty due to the phenomenon of chaotic wave scattering. The APOD resolution of the SDF dataset of composite beams extracts the actual bending curvature distribution, indicating directly the structural health state footprint imposed by the thermodynamical manufacturing process. The introduced data augmentation operations and the physics extraction by the APOD reduction analysis constitute the basis for a sound data science in engineering mechanics-physics with impact on SHM.
Machine-Learning Based Fault Diagnosis for a Rotordynamic System Using Multibody Simulations
Machine-learning based fault diagnosis plays an important role in condition monitoring for rotating machinery to prevent systems from catastrophic faults. It is important to note that the performance of data-driven methods relies highly on a large quantity of training fault data. Since rotating machinery operates under normal condition most of the time, collecting sufficient fault data from experiments takes a huge amount of time and expense, and under various operating conditions. To overcome the fault data insufficiency, building a virtual testbed for generating fault data is a promising way in bridging the gap between data requirement and prediction accuracy. Many simplified dynamic models have been proposed to generate a single fault on some rotordynamic systems. These methods, however, cannot reflect complex operation conditions such as variant rotation speed or multi-faults. To better reveal vibration responses of local defects, this research aims to establish a multibody dynamics (MBD) model that can simultaneously analyze complete dynamic behavior and simulate a wider range of fault scenarios. In this research, a simulation-driven fault diagnosis method is proposed to generate the simulation fault data. Firstly, a rigid-flexible hybrid model of a single-rotor-bearing system is established using MSC ADAMS, which is based on MBD and finite element analysis. Different fault conditions are simulated including outer race bearing faults, inner race bearing faults, and rolling element faults. After generating fault data, a timefrequency feature extraction method is developed based on Hilbert envelope and wavelet packet decomposition, extracting a large amount of features from the original signals. In addition, an autoencoder model is built to highlight the critical features, enhancing the performance of the classifier. This feature extraction is made to obtain fault-related features, which train the machine learning classifiers for discriminating the fault categories. To validate the simulation results, the Case Western Reserve University (CWRU) bearing dataset that has been widely accepted as a standard reference is introduced. A comparison of bearing fault frequencies between simulations and the CWRU dataset is then conducted. Meanwhile, a transfer learning method is applied using the CWRU dataset to fine tune the fault diagnosis classifier. This research lays a solid foundation for future development of a digital twin and simulation-driven transfer learning for fault diagnosis of rotating machines.
Research on Intelligent Identification of Vortex-Induced Vibration of Stay Cables Based on Multi-Dimensional Feature Extraction and Deep Learning
The multimodal vortex-induced vibration (VIV) of stay cables in long-span cable- stayed bridges poses a severe challenge to the real-time identification capabilities of bridge health monitoring systems due to its complex dynamic characteristics and multifactor coupling effects. Existing methods mostly rely on single-dimensional features (such as time-domain or frequency-domain indices) and fail to systematically integrate environmental and structural parameters, thereby limiting identification accuracy. To address this issue, this study proposes an intelligent identification framework based on multidimensional feature extraction and deep learning to enhance the accuracy and real-time performance of VIV identification. Based on the analysis of health monitoring data from a cable-stayed bridge with a main span of 620 meters, a statistical analysis of environmental parameters sensitive to VIV was conducted to determine the wind speed and direction ranges most likely to trigger VIV. On this basis, the time-domain and frequency-domain characteristics of VIV were analyzed, and a multidimensional feature vector was constructed. Subsequently, a BiLSTM-MHA model combining bidirectional long short-term memory networks (BiLSTM) and multi- head attention (MHA) mechanisms was developed to dynamically capture the time- frequency features of vibration signals. Finally, leveraging this model, accurate identification of various stages of VIV was successfully achieved, significantly enhancing the intelligence level of bridge health monitoring and providing an effective technical approach for real-time detection and control of VIV.
Artificial Intelligence-Based SHM Approach for Fatigue Damage Assessment
Periodic inspections are common to find flaws in aircraft structures. Continuous monitoring of the structural integrity using Structural Health Monitoring (SHM) is desirable, as undetectable damage can occur at any time during a structure’s lifespan. Numerous SHM technologies have been developed to identify and to evaluate defects in structures. Using data-driven analysis methods, such as artificial intelligence (AI), information about the condition of structures can be extracted from large amounts of complex measured data. However, current AI-based damage assessment methods are often limited to simple, well-defined structures. This paper presents an AI-based approach for damage assessment on a full-scale fuselage panel with a complex substructure. An aluminum panel tested by the Federal Aviation Administration (FAA) at the William J Hughes Technical Center is used to explore new applications of artificial neural networks (ANN) by different input scenarios and classification concepts, for damage detection and damage level prediction. Initial damage is created by a sawcut. The panel is fatigued through cyclic loading, causing the crack to propagate, providing comprehensive sensor data responses due to the crack growth. The experimental setup involves Acellent Technologies piezoelectric (PZT) sensors symmetrically placed on opposite sides of the sawcut, capturing pitch-catch signals. The ANN model utilizes damage indices derived from the sensor signals to detect and assess the damage. This research highlights the potential of AI-based approaches in enhancing the capabilities of SHM systems, paving the way for more reliable and efficient structural monitoring.
Unsupervised Crack Detection in Large Stamped Metal Products Via Spatial Transformer and U2-Net with Masked Attention and Patch Positional Encoding
Crack detection is crucial for the quality assessment of metal products. While supervised learning methods are commonly employed to automate this process, their performance is constrained by the limited availability of large and diverse crack datasets. Unsupervised learning has shown exceptional performance in anomaly detection by relying solely on healthy data during training. For large products, cracks are small in size compared to the entire object, making image patches a more suitable representation. However, the large diversity of image patches introduces significant challenges for unsupervised learning methods. This study proposes a spatial transformer network utilizing binary masks from Segment Anything Model to reduce the complexity of image patches. To perform crack detection, we propose a U2Net- based model with neighbor masked attention at different scales to learn the distribution of healthy data from discrete representations extracted by Vector Quantized Variational Autoencoder (VQ-VAE). Additionally, patch positional encoding is incorporated to enhance the model’s ability to match patches and the distribution. Features that deviate from the learned distribution are identified as cracks. We resemble a real manufacturing setting and capture images from large stamped metal panels. Comprehensive experiments are conducted, and results indicate the effectiveness of the proposed method and individual modules.
Data Assimilation to Estimate the State of Partially Ionized Plasmas in Space Propulsion Systems
Spacecraft electric propulsion plays a critical role in in-space missions. Understanding the state of the ionized gas, i.e., plasmas, is important for the characterization of thruster performance and the lifetime. In this talk, we present the recent development of data assimilation (DA) framework that estimates state variables and parameters obtained from a physics-based dynamical model with noisy experimental data. We have developed DA techniques based on variants of Kalman filters to estimate state and parameters in plasma systems governed by coupled nonlinear ordinary and partial differential equations.
Bridging the Knowledge Gap in NDE 4.0: AI-Augmented Insights Through Retrieval-Augmented Generation (RAG)
The rapid digitization of nondestructive evaluation (NDE) has led to fragmented, siloed knowledge across formats (manuals, reports, standards) and a generation gap among practitioners. In this work we present an AI-augmented knowledge assistant for NDE 4.0 that uses Retrieval-Augmented Generation (RAG) to provide real-time technical guidance. Our system ingests diverse NDE documents, chunks and semantically indexes their content (using MiniLM-L6-v2 and FAISS), and at query time retrieves relevant context before querying a language model (Google Gemini). We describe the system architecture and implementation, report metrics (high retrieval precision, sub-second latency, minimal API cost), and demonstrate a sample NDT use case (e.g. explaining ultrasonic testing and magnetic particle inspection steps). This AI-RAG assistant can help inspectors access “know-how and know-why” on demand, enabling continuous learning and upskilling in line with NDE 4.0. Future work includes multilingual support, voice interfaces, and LMS integration.
Satellite-Enhanced Structural Digital Twin for Bridge Health Monitoring
This paper investigates the feasibility of integrating satellite data into structural health monitoring (SHM) systems for transport infrastructure subjected to extreme loads. By combining InSAR satellite data, structural digital twin simulations, and advanced post-processing techniques, the study evaluates the potential of spaceborne remote sensing to provide insights into structural displacement and overall health conditions. A real-world pilot was conducted on the Kalix Bridge in Sweden using Sentinel-1-derived InSAR time series from the European Ground Motion Service (EGMS). The analysis demonstrated consistent uplift trends and highlighted the critical role of coherence-based filtering in extracting reliable displacement data. The results offer a foundation for future research in satellite-based SHM and underscore the importance of integrating geospatial and structural data for infrastructure resilience.
Mahine Learning-Based Fault Detection of Electric Motor Bearings Using Vibration Analysis
Bearing faults are a leading cause of electric motor failure, often resulting in costly downtime and maintenance. This research proposes a machine learning-based framework for early fault detection using vibration signal analysis. Vibration data from a bearing test rig under five operating conditions were collected and transformed using Fast Fourier Transform (FFT) to extract key frequency-domain features, while Principal Component Analysis (PCA) was used to reduce data complexity. Five machine learning models were evaluated, with the Medium Gaussian SVM achieving the highest accuracy of 96.8%. Although PCA-based models like Ensemble Subspace KNN had slightly lower accuracy (94.0%), they significantly reduced training time. The findings highlight a trade-off between classification accuracy and computational efficiency, with FFT offering superior fault-specific pattern retention and PCA enhancing speed. The proposed approach offers a scalable solution for real-time, predictive maintenance, and future work may explore hybrid feature extraction or deep learning to further improve performance.
Phase Change Monitoring for Energy Content Determination in Gelled Latent Heat Storage Using Electromechanical Impedance Spectra
To mitigate climate change, a more sustainable and efficient use of resources is cru- cial. As approximately 50 % of energy consumption is attributable to thermal appli- cations, insulation materials and thermal management offer great potential for savings. Latent heat storage materials (phase change materials - PCM) are suitable for storing and releasing large amounts of thermal energy, e.g. in industrial waste heat recovery, off-peak power generation, solar power plants, food industry, spacecrafts and medicine. In 2022, we were able to show that the use of sorbitol-based organogelators together with polyolefins allows the production of form-stable gels from organic PCMs. Un- like traditional PCMs, these gels do not flow in the molten state and show very low leakage and high energy storage capacity. This work presents a novel approach to deter- mine the stored energy content in these form-stable PCMs using the electro-mechanical impedance (EMI) spectrum, for which a patent was recently filed. Piezoelectric ele- ments, embedded within form-stabilized, organic PCMs, can detect and quantify the de- gree of phase transition from solid to liquid (and vice versa). Additionally, this method can potentially monitor the condition of implemented PCM systems, as the detection of potential leakage and degrading of materials is a common application for EMI and ultrasonic waves.
Demonstration of Using Digital Twins for Offshore Wind Turbine Foundation Monitoring
Operation and maintenance of offshore wind turbines (OWTs) require accurate, robust, and continuous monitoring of all components. In this sense, nonlinear and vibration-based techniques have been proposed to enable remote monitoring and deterioration analysis; however, such works tend to focus on the superstructures while simplifying the foundation behavior. Conversely, structural health monitoring tools that incorporate the foundation tend to be deterministic and rely on modal analysis, which limits their application to linear and well-conditioned systems. This paper attempts to bridge these gaps and presents a digital twin for monopile-supported OWTs that includes a comprehensive foundation model and a nonlinear updating method. The digital twin is a beam finite element model with soil-structure interaction of the Winkler type, and it employs nonlinear optimization to identify operational soil parameters using acceleration data. This approach allows time-domain and continuous monitoring of OWTs’ foundation and global responses. Results show that the proposed methodology accurately identifies soil parameters and tracks their changes.
Description of Different Technologies for Ship’s Hull Structural Health Monitoring
Traditional ship maintenance protocols rely primarily on preventive strategies based on scheduled inspections or accumulated operational cycles. While effective in ensuring baseline safety, these approaches are often inefficient, leading to unnecessary maintenance or unanticipated failures. Structural Health Monitoring (SHM) systems enable a paradigm shift towards predictive maintenance by continuously assessing the ship’s condition in real time. This paper, developed under the scope of the EDF dTHOR project, reviews current and emerging sensor technologies used for hull SHM. Particular attention is paid to acoustic signature monitoring, a key element in defense applications.
Sensor-Based Shutdown Event Detection in Monopile-Supported Offshore Wind Turbines Using Machine Learning
Offshore wind turbines (OWTs) present significant challenges for structural health monitoring (SHM) because of varying environmental and operational conditions. This paper proposes a machine learning (ML) approach to detect emergency shutdown events using sensor data located at the top of the foundation. Shutdowns, caused by rapid blade feathering, induce transient stresses, which can provide valuable insights into the structural integrity of the structure when measured. An integrated finite element model is used to simulate OWT load states under varying conditions, generating synthetic data for supervised learning. A k-Nearest Neighbors classifier is trained to distinguish between nominal, shutdown, and parked states. Results suggest the classifier performs well across different environmental scenarios, including unseen conditions. While low wind speeds show reduced accuracy, this is not critical as the method reliably identifies transience in significant shutdown events, achieving its primary objective. The results demonstrate the feasibility of using ML for event detection in OWTs from foundationlevel accelerometers and inclinometers, with potential applications in SHM and digital twin updating approaches.
Unsupervised Feature Engineering in Imbalanced and Unlabeled Mooring Monitoring Data for Augmentation and Translation by Hierarchical Variational Approach
The stability of mooring systems in floating offshore wind turbines (FOWTs) is essential, as any deterioration affects platform performance and overall efficiency. Machinelearning monitoring of FOWTs requires comprehensive dynamic response data derived from various mooring systems. Data collection should encompass a variety of health states, operational scenarios, and metocean conditions. Data is scarce in practice, especially concerning damage-associated information. Additionally, the existing imbalanced dataset lacks clear labels, making it difficult to differentiate between healthy (majority) and damage-associated (minority) instances. We propose a novel deep generative model (DGM) to efficiently deal with the challenge of unsupervised labeling of imbalanced data in the initial stage. It will subsequently be used to augment the labeled data from the previous stage and generate damage-associated data for a new (target) mooring system based solely on the healthy records of that system. The conditional hierarchical variational autoencoder (CHVAE) is developed using a diffusion probabilistic architecture during a pretrain-finetune training procedure. During the pre-training stage, trained on imbalanced, unlabeled data, we employed an unsupervised feature engineering approach in the latent space created to identify the healthy (majority) and damaged (minority) data. In the fine-tuning stage, the nonlinear relationship between healthy and minority-damaged responses of a mooring system is obtained, considered as the source domain across diverse sea states. Subsequently, by employing the healthy data from the target mooring system, the CHVAE can generate real-scale damaged responses of the system across diverse operational and environmental conditions. An analysis is conducted to evaluate the similarity between the simulated records produced by OpenFast on the OC4-DeepCWind FOWT benchmark and those generated by the CHVAE framework, utilizing visual, statistical, and behavioral methods. Furthermore, the proposed DGM’s performance is first assessed using MNIST benchmark image dataset to evaluate its effectiveness for unsupervised labeling and data augmentation. The generated records for unobserved random sea states closely resemble real-world dynamic behaviors during downstream binary classification, illustrating the effectiveness and versatility of the proposed DGM, CHVAE.
Structural Health Monitoring of Steel Strip Reinforced Thermoplastic Pipe Under Internal Pressure
This study aims to support structural health monitoring (SHM) of offshore pipelines by characterizing the strain-pressure behavior of steel strip reinforced thermoplastic pipe (SSRTP). An innovative split-ring test method and validated finite element model (FEM) are used to analyze the progressive strain development under internal pressure. The study further explores the influence of interlayer friction and winding angle on the strain response, providing essential insights for accurate SHM interpretation and damage prediction of offshore pipelines. It is found that the friction between layers effectively reduces the hoop strain on the external surface. A large winding angle results in smaller hoop stains.
Improving Image Resolution for Drone-Borne Inspection of Wind Turbine Blades
Inspections of wind turbine blades, both exterior and interior, present significant challenges due to limited accessibility as well as physical and measurement constraints. Key barriers to effective monitoring include the lack of suitable high-resolution distributed sensors, high cost and safety risks associated with up-tower technician deployment, limited availability of skilled personnel, and confined spaces involved in internal inspections. Advanced technologies such as drones, computer vision, and artificial intelligence offer transformative potential by enabling faster, more accurate, and cost-efficient inspections, leading to improved reliability and reduced overall energy costs. However, drone-based inspections typically require shutting down turbines to ensure the safe operation of unmanned aerial vehicles (UAVs) near the assets. To address this limitation, this research introduces a novel method referred to as Stack-Average (SA), which combines multiple images of a wind turbine to produce a super-resolved image with higher resolution. The SA method facilitates damage detection from greater distances (50+ meters) and eliminates the need to shut down turbines during inspections. The performance of the SA method was evaluated on a 1.5- meter section of a wind turbine blade. Results demonstrated the method’s accuracy in identifying various damage types as a function of image capture distance and the number of images used for super-resolution. This approach has the potential to improve image resolution for a given working distance and thereby enhance remote monitoring of wind turbine blades or other large-scale structures.
Seasonal Environmental Effects on Structural Behaviors of a Prestressed Double-T Slab
By analyzing the temperature-strain relationships of structural components, key structural properties, such as the coefficient of thermal expansion (CTE), and boundary and continuity conditions of structures, can be assessed. However, these relationships can be influenced by non-structural factors such as variability of ambient environmental conditions, especially ambient temperature variations, which can obscure structural responses and complicate the identification of fundamental properties. This study presents a preliminary study on the seasonal environmental effects on structural behaviors of a prestressed slab with a double-T cross-section through a series of static and dynamic loading tests conducted at different times of the year. Embedded long-gauge strain sensors are utilized to monitor temperature and strain continuously, capturing the slab's behavior under real-world environmental variations. By analyzing the temperature-strain relationships derived from on-site loading test measurements, the study aims to evaluate how different environmental temperatures affect the structural behaviors of the prestressed double-T slab, which can reflect the structural properties, such as CTE, boundary and continuity conditions. The results demonstrate the ability of long-gauge sensors to accurately capture the slab’s strain distributions under varying loading and temperature conditions. Seasonal effects are found to significantly influence the structural behavior of the slab, emphasizing the necessity of incorporating environmental factors into structural health monitoring practices. This research offers insights into the interplay between environmental conditions and structural properties, contributing to the development of more effective monitoring and management strategies for concrete structural components under seasonal environmental effects.
Levee and Embankment Monitoring with Novel Embeddable Passive RF Sensing System
Levees and embankments are important infrastructure for protecting industrial, commercial, residential, and agricultural regions from flooding. As global temperatures increase due to climate change, flooding is expected to become more severe in the future. As flooding occurs more often with higher levels and streamflow, levee structures face a greater risk from events exceeding prior design expectations. Existing monitoring technologies either focus on discrete or continuous measurements with complicated installation, which is a challenge in large-scale implementation. Therefore, it is of interest to develop a levee monitoring system to effectively implement on a large scale for assessing and maintaining the levee condition under extreme events. This presentation includes preliminary research on a novel embeddable passive radio frequency (RF) sensing system and its applicability in levee monitoring applications. Previous studies have demonstrated the sensing system’s ability to detect changes in displacement and water content. This study focuses on implementation challenges, particularly the use of a distant exciter to power passive RF sensors. The exciter is expected to be carried by unmanned aerial vehicles (UAV), positioned a few meters from the levee surface. Controlled laboratory experiments are conducted to analyze the exciter’s effects. The main challenge of this research includes replicating the potential trajectory of the exciter and identifying the influence of the exciter’s position on the system performance. The results demonstrate the impact of the exciter’s position on the collected data. These findings demonstrate the feasibility of the RF sensing system for levee monitoring while addressing the challenges associated with UAV-mounted exciters. By further investigating and addressing these challenges, the system can be made a step closer to real-world implementations.
A New Comparison Index of Estimation Accuracy for Wave Load-Based DBM Superposition Method on Marine Structures
The hull stress monitoring (HSM) system, which is the early form of sensor-based structure health monitoring (SHM) in the marine industry, has the limitation that it can only monitor the response at the location where the sensor, long-based strain gauge (LBSG), is attached. To overcome this limitation, the wave load-based distortion basis mode (DBM) superposition method was proposed. This method requires numerous case studies to find the optimal LBSG configuration with best estimation accuracy. To facilitate those studies, efficient way of comparison on the estimation accuracy between cases is necessary. In this study, a new index that can consider both the amplitude and phase shift of the response is introduced. With this index, study with over 150,000 cases are performed for a semi-submersible structure, and the estimation accuracy is compared according to the total number of LBSGs.
Machine Learning-Based Predictive Models for Multi-Hazard Response Analysis of Offshore Wind Turbines
The growing deployment of offshore wind turbines (OWTs) demands efficient structural health monitoring (SHM) strategies to ensure safety and reliability under multi-hazard conditions. OWTs experience complex dynamic interactions from wind, waves, and seismic forces, making real-time predictive capabilities essential for condition-based maintenance. However, high-fidelity simulations such as finite element models (FEM) are computationally intensive for real-time use. This study presents an ensemble machine learning framework as a fast surrogate for FEM-based simulations, enabling rapid prediction of key structural responses including hub displacement, acceleration, and pile head rotation. The model captures intricate relationships between key system parameters, including wind turbine capacity, offshore environmental conditions (wind speed, wave height, wave period, and ground acceleration), and site- specific factors (water depth, soil properties). Model performance and generalisation are systematically assessed using a statistical learning workflow. The proposed surrogate model significantly reduces computational cost while retaining predictive accuracy, supporting real-time SHM integration and facilitating informed decision-making. This approach advances AI-driven SHM for offshore renewables, providing scalable, data- efficient solutions to enhance resilience, operational efficiency, and sustainability.
On the Excitability of Zero-Group Velocity Lamb Modes Using Normal Mode Expansion Method
This study employs the normal mode expansion (NME) method to investigate both symmetric and anti-symmetric zero-group velocity (ZGV) modes. Since a general loading case can always be decomposed into the superposition of the symmetric and anti-symmetric parts corresponding to the symmetric and anti-symmetric modes. The research considers a general loading case and the shear lag model for an adhesive bonding layer to calculate the amplitudes of the generated Lamb modes within and outside of the loading zone. By targeting symmetric (S-) and anti-symmetric (A-) type ZGV modes, the study uses exact analytical solutions and numerical results to validate the effectiveness of the model, ensuring a comprehensive understanding of the behavior of ZGV modes. The influence of the piezoelectric patches’ length on the excitability of S- and A-type ZGV modes is also examined. The length of the PZT is a significant factor in determining how effectively the energy can be captured by the targeted modes. Numerical simulations are conducted to observe the behavior of these modes outside the loading zone, providing a broader perspective on their dynamics. Additionally, the study utilizes 2D fast Fourier transform (2D-FFT) results to verify the resonance characteristics of the ZGV modes at the aiming frequency. These results are essential in confirming the theoretical predictions and understanding the practical implications of the ZGV modes. This approach offers valuable insights into the physical behavior of ZGV Lamb modes and their potential applications in various fields.
Identifying Debonding Regions in a Composite Radiative Cooling Film Bonded to a Steel Plate
This study presents a method to map the regions of debonding of a thin composite film attached to a steel plate based on the changes in Lamb wave signatures. The film in discussion is a passive radiative cooling film that can be coated on the top of metallic roof shingles to reflect the sunlight and subsequently cool the building. It is an energyefficient building material that can potentially reduce building energy consumption by up to 30%. Its effectiveness can, however, be decreased by damage or debonding of the film from the substructure. Hence, it is essential to identify the regions of debonding/ damage in a timely manner for rectification. Here, we demonstrate a method to map the regions of debonding based on changes observed in Lamb waves when they pass through a debonded region. When the Lamb waves pass through this film, they get attenuated and arrive with a slight delay. By quantifying the changes in the wavepackets, the debonded regions can be mapped tomographically. With this as the basis, experiments were conducted on a steel plate bonded with the film to demonstrate the mapping of debonded regions. A small section of the film was removed to simulate a damaged scenario. PZT discs were bonded to the structure along the edges of the square to create a dense network of paths passing through the film. Along each path, signals are acquired in pristine and damaged conditions to estimate the damage index for each path. The Reconstruction Algorithm for Probabilistic Inspection of Damage (RAPID) technique was employed to construct the map, where high-intensity regions denoted areas with a higher likelihood of debonding. The obtained map was compared with the structure to validate the method and establish it as a viable option. Through a successful demonstration, this work lays the foundations for identifying and quantifying regions of film debonding.
Monitoring Circumferential Welds in Canisters with Helical Ultrasonic Guided Waves
Welded regions in canisters that are used to store industrial chemicals, fuels, gases, spent nuclear fuels, etc., are susceptible to damage due to stress corrosion cracking and fatigue failure. Hence, monitoring their integrity over time is essential to prevent leaks. One way to monitor the canister welds’ integrity is through active SHM with helicalguided ultrasonic waves (HGUW). When HGUW interact with defects in welds, their signature is altered, and based on this, the regions of defects can be located and mapped. This work demonstrates the mapping of damaged regions in circumferential welds in canisters using HGUW. A canister cut section, made by circumferentially welding two rings, was used as the test structure to demonstrate the mapping methodology. Two circumferential PZT arrays, each one on the opposing side of the weld, provided a network of paths that enabled monitoring of the weld all around the circumference. The presence or absence of damage in any section of the weld can be determined by analyzing the wave propagating along a path passing through the specific region. By combining information from all such paths, a map indicating the location of damages was constructed. Accordingly, waveforms from all paths of the network were acquired, and the information from them was used to generate a map via the Reconstruction Algorithm for Probabilistic Inspection of Damage (RAPID) method to locate the damaged area. The successful mapping of the damaged region established this technique as a valid option for the long-term monitoring of circumferential welds.
Steps for Characterizing Crack-Like Defects in Real-World Hollow Cylindrical Structures from Scattering of Helically Guided Ultrasonic Waves
Recently, a method called the stepped wavelength scattering analysis was proposed for tracking the growth of circumferential crack-like defects in hollow cylindrical structures. As the name implies, this method is based on the scattering of helical guided ultrasonic waves (HGUW). To identify the defect length, the method quantifies the perceivable change in the nature of interactions as the wavelength-to-crack length ratio goes from being less than one to equal to or greater than one. Specifically, when the wavelength is smaller than the defect size, the scattering directivity plots are well-explainable by ray-like interactions. However, when the wavelength becomes comparable or greater than the defect size, the interaction is diffraction-dominated. This change in scattering pattern with respect to wavelength-to-crack length was quantified to establish a criterion for estimating the defect length. Building on this foundation, this paper discusses the steps for extending the method for practical implementation in real-world structures. As such, a three-step strategy with circumferential arrays of transducers is proposed. The steps include (1) locating the defect through HGUW tomography, (2) estimating its orientation from the angular distribution of scattered lobes, and (3) determining its length using the stepped-wavelength scattering analysis. The discussion also highlights a key requirement for implementation: reconstructing full circular-array responses from circumferential measurements. Once a DNN-based model is developed to accomplish this, the proposed three-step framework can enable practical in-situ monitoring and tracking of crack-like defects in hollow cylindrical structures.
Ultrasonic Guided Wave Based Damage Detection of Engine Composite Fan Blades
Engine composite fan blades are subjected to tremendous centrifugal and aerodynamic forces for long periods of time, resulting in blade cracking and delamination. Ultrasonic guided wave detection technology in structural health inspection technology has the advantages of wide coverage and high detection accuracy, and has a very high potential for application in the health monitoring of blades. However, the variable curvature and material anisotropy of the blade can affect the propagation of the guided wave, which in turn affects the accuracy of damage quantification and localization. In order to mitigate this effect, an ultrasonic guided wave detection method applicable to engine blade structures is proposed in this paper to localize and quantify the damage. The damage likelihood lines were determined based on the ratio of the damage index(DI) of the signals acquired by each pair of sensors, and the damage was localized by the intersection of multiple damage likelihood lines. The size of the damage was assessed as the mean value of the two largest damage factors. This method was validated on a test platform simulating the rotating operating environment of an engine blade, where damage was simulated by bonding a vacuum sealant to the blade. The results show that the proposed method for damage localization and quantification is effective and suitable for damage detection of composite blades.
Dispersion Properties of Micropolar Plates Through the Partial Wave Technique
The focus of this study is on the dynamic problem of free guided propagation of harmonic elastic waves in homogeneous plates modeled as a linear micropolar continuum, in the absence of dissipation. The plate is modeled as an infinite layer with finite thickness, bounded by two parallel traction-free planes. The analysis is carried out using the analytical partial wave technique, which returns the dispersion relation in terms of wavenumber as a function of frequency, as well as the wavemode shapes. The dispersion relation for the micropolar continuum differs markedly from that of a Cauchy continuum, above all for the presence of microrotation modes – unique to the Cosserat continuum. Morphological modification of the standing wavemodes, as well as the occurrence of internal resonance conditions and inter-wave transfer of mechanical energy, are observed among microrotation waveforms and other waveforms.
Enabling Distributed, Multifunctional SHM Through Integrated Metamaterials and Piezoelectrics
Structural Health Monitoring (SHM) is essential for ensuring the safety and longevity of critical infrastructure. Traditional methods face limitations in spatial coverage, susceptibility to environmental factors, and complex data interpretation. To address these challenges, this research proposes an intersection of mechanical metamaterials with embedded piezoelectric sensors. Metamaterials enable distributed sensing while maintaining the load-bearing capacity of a structure. Piezoelectric materials provide passive strain/vibration sensing and vibration-damping capabilities. Integrating piezoelectric elements with a tailored re-entrant mechanical metamaterial design enables smart support system beam condition monitoring. Experimental validation was conducted on an aluminium beam supported by a pin and roller supports, exploring the feasibility and potential of this smart SHM system. This approach offers distributed sensing, multi-functionality, and more resilient built environments that continuously monitor their structural health and adapt as needed. This paper demonstrates the potential of a smart engineering support system for SHM.
Ultrasonic Guided Wave Based Intelligent Structural Health Monitoring of Pressure Vessels
Pressure vessels are critical components in industries such as hydrogen energy, cryogenics, and oil and gas, where they are subjected to harsh environmental and operational conditions. Corrosion remains a predominant challenge, gradually degrading structural integrity and increasing the risk of catastrophic failures. Conventional inspection techniques, such as visual assessments and periodic ultrasonic testing, often lack real-time monitoring capabilities, leading to delayed damage detection and unplanned maintenance. To address these limitations, this study explores the application of ultrasonic guided waves for autonomous structural health monitoring of pressure vessels. The research systematically examines the propagation characteristics of guided waves in stainless steel pressure vessels, focusing on their interaction with corrosion-induced defects. Through controlled experimental investigations, mode-specific amplitude variations are analyzed to establish their sensitivity to localized damage. Additionally, the study evaluates how internal pressure fluctuations influence guided wave behavior, providing insights into the dynamic response of the vessel structure under operational loads. By integrating guided wave analysis with an autonomous monitoring framework, the proposed approach enhances the early detection of corrosion damage, enabling predictive maintenance strategies. The findings contribute to the development of intelligent condition-based monitoring systems capable of real-time structural assessment, improving the safety, efficiency, and longevity of pressure vessels in demanding industrial environments.
Influence of Lamb Wave and Structural Parameters on Damage Localization: A Comprehensive Analysis Using Numerical Simulations
The structural health monitoring (SHM) field extensively uses Lamb waves for defect detection due to their extended propagation capability and sensitivity to various damage types. However, the Lamb wave exhibits a few limitations caused by its multimode characteristics and intrinsic dispersion behaviour. It is challenging to localize structural defects due to the presence of many dispersive modes at a particular frequency band. Varying the central frequency of a Lamb wave across its dispersion curve range for a specific structural thickness is investigatedhere to study its behaviour in damage localization. Numerical simulations using the finite element method in three dimensions show fifteen aluminium plate analyses in the time domain. A throughthickness hole is created as a defect in the plate. The least dispersed symmetric Lamb mode (S0) with an increasing central frequency in the specified range is actuated in each scenario. The output of out-of-plane signals is utilized in damage location imaging. Lamb wave frequency accuracy varies significantly, as seen in the images of the damage areas. The damage area grows as the central frequency of the actuation signal increases. The precise location is estimated in each of the fifteen cases; however, the projected damage zone differs. According to the imaging study, damage localization is improved using low-frequency Lamb modes.
Experimentally Derived Weight Distribution Function for Probabilistic Damage Imaging Using Guided Waves
The reconstruction algorithm for probabilistic inspection of defects (RAPID) is widely employed to localize damage in thin-walled structures using guided waves. This method constructs probabilistic damage maps using damage indices (DIs) of the sensing paths in a sensor network and associated weight distribution functions (WDFs). Accurate weight distribution function (WDF) estimation is crucial for determining the probability of dam- age at a given point. Several WDFs have been proposed, including linear, Gaussian, and inverse power law functions, but they rely on assumptions without any experimental validation. We experimentally determine the WDF as the ratio of the DIs of a sensing path due to damage at a given point to its value when the damage is situated on the sensing path orthogonal to that point. Results reveal that the experimentally obtained variation of the damage index ratio does not conform to any of the existing WDF models for either baseline-based or baseline-free methods. Based on experimental data, we propose a simple exponential decay-type WDF, which is shown to be applicable for baseline-based methods and baseline-free refined time reversal method (RTRM) (developed by the second authors group). Thus, the newly proposed WDF provides an accurate estimate of damage probability for both baseline-based methods and the baseline-free RTRM.
In-Situ Characterization of Laser Shock Dismantling Process Using Ultrasonic Guided Waves Sensed by Bonded Piezoelectric Transducers
Laser shock dismantling allows to selectively separate the various plies of a composite material in order to sort aged or damaged ones from virgin or healthier ones and to build new composite materials from these recycled plies. However, following in situ the laser shock process is in practice hard to achieve due to the very short laser pulse duration (around 10 ns) and to the large associated energy density (around 10 GW/cm²). Piezoelectric elements are low cost transducers that can be easily bonded to composite structures and that are able to sense ultrasonic guided waves propagating in such structures in the frequency range below 500 kHz. Consequently, if a composite coupon enduring laser shock dismantling is equipped with piezoelectric elements, those sensors should potentially be able to collect dynamical signals directly following laser shock. In order to validate this approach, an experimental campaign has been carried out where a composite coupon has been impacted by laser shocks of increasing intensity in order to achieve delamination damages. Dynamical signals resulting from those consecutive laser shocks have been measured by the piezoelectric transducers. The signal collected by the piezoelectric transducers during a laser shock are shown to be exploitable despite the short laser shock duration in comparison with sensor bandwidth and can be reliably used to locate laser shock position and to estimate its intensity. Such an in-situ process monitoring approach could thus allow to ease the deployment and practical use of laser shock technology for composite materials dismantling and recycling purposes.
Ultrasonic Guided Wave-Based Structural Health Monitoring for Early Detection and Localization of Multiple Damages in Composite Wind-Turbine Blades
Wind energy plays a pivotal role in the transition to renewable energy sources. However, the reliability of wind turbine blades (WTBs) is often compromised by damage. This urges the increasing need for advanced Structural Health Monitoring (SHM) techniques to ensure the durability and reliability of WTBs. The current study investigates the application of the Ultrasonic Guided Wave (UGW) technique as a nondestructive evaluation (NDE) method for the early detection and localisation of multiple damages in composite WTBs. In this research, a network of three piezoelectric patches is strategically positioned on the blade’s surface to excite guided waves and capture the scattered signals. The study focuses on localising the damages, such as impact damage, surface cracks and their combination. Finite Element (FE) modelling is utilised to simulate wave propagation in the complex composite WTB. A novel damage index mapping approach, based on signal energy difference and time-of-flight (ToF) analysis, is employed to approximate defect locations. The proposed methodology effectively demonstrates the capability to detect and locate the various damage types with significant accuracy. This research contributes to the advancement of intelligent SHM systems for wind energy applications, facilitating autonomous, data-driven maintenance strategies for efficient damage assessment.
Investigation of Damage-Induced Ultrasound Signals Emitted by Fatigue Cracks in Steel Marine Structures
In this paper, an investigation of the characterization of fatigue damage-induced signals by means of Acoustic Emission (AE) monitoring is presented. The objective is to establish a correlation between AE signals and fatigue crack growth data. To achieve this, small-scale fatigue experiments have been performed. The test consists of cyclic loading of standardized compact test (CT) specimens at room temperature. Damage- induced ultrasound signals were continuously measured using four AE transducers. The results suggest that AE signals emitted by fatigue crack growth from the initiation moment can be detected with a satisfactory signal-to-noise ratio. A multi-parameter analysis including amplitude, counts and hit rate of AE data in correlation with crack growth data was performed. Three stages of fatigue crack growth were identified, offering a basis for further damage characterisation using AE monitoring.
Amplification and Filtering of Shear Horizontal Waves Using Flexural Resonance-Based Metamaterials
We investigate the use of a flexural resonance based metamaterial to focus shearhorizontal modes in a thin-walled structure. Shear horizontal waves have many advantages for structural heath monitoring, but their low amplitude and in-plane displacements can make them difficult to detect with sufficient signal-to-noise ratios. Through simulations, we demonstrate that resonance based elastic metamaterials can be used to amplify these modes prior to capture.
A Study on Guided Wave Propagation in Quasicrystal Plates
Guided wave-based Structural Health Monitoring (SHM) is a highly promising approach for assessing the structural integrity of large-scale engineering systems. This method offers significant advantages, including rapid and extensive area monitoring, long-distance wave propagation, the capability to detect internal damage, and low energy consumption. Quasicrystals are recent advancements in materials characterized by low frictional and adhesion coefficients, along with high abrasion and thermal resistance. These properties make them highly suitable for coating surfaces in various engineering applications. In quasicrystals, the quasi-periodic atomic arrangement introduces additional phason displacement modes alongside phonon modes, presenting a unique challenge in guided wave analysis. In this work, the semi-analytical finite element (SAFE) method has been implemented for computing dispersion curves, phase and group velocities for quasicrystals. These methods have advantage in wave propagation analysis for complex cross section where analytical solution is difficult to obtain. Using the SAFE, a novel eigenvalue formulation has been developed for established quasicrystal models in literature: Bak’s model and Lubensky’s model. This study focuses on understanding how phason modes behave in Lamb wave analysis and also understanding how phononphason coupling constant has effect on Lamb wave analysis. Results have been obtained for various phason elastic constants reported in literature.
Distinctive Features of Zero Group Velocity Guided Waves for Damage Detection
This paper presents the recent progress from Active Materials and Intelligent Structures (AMIS) lab at Shanghai Jiao Tong University on utilizing special features of ZGV guided waves for damage detection. As the first part of this investigation, the Electro-Mechanical Impedance (EMI) method is studied, focusing on a special “trembling” phenomenon in the spectrum at the ZGV resonance. Finite Element Modeling (FEM) is performed to showcase such an interesting feature. It is found that the trembling feature following the resonance peak can serve as a special signature for ZGV modes identification and damage evaluation. Experiments on delamination detection is performed to demonstrate the superb sensitivity of the ZGV impedance technique. As the second part of this investigation, the nonlinear ZGV waves for fatigue damage detection is studied. The nonlinear interactions between guided waves and the fatigue crack generate second higher harmonics. Residing the second harmonics at the ZGV mode allows the nonlinear wave features to accumulate in the vicinity of the cracks. FEM simulations are carried out to demonstrate and analyze the special phenomenon. It is found that the second harmonic ZGV renders a much stronger nonlinear signature, which considerably enhances the damage detection sensitivity. The paper finishes with summary, concluding remarks, and suggestions for future work.
Time-Frequency Analysis on Numerical and Experimental Ultrasonic Guided Wave Scattering for Quantitative Damage Characterization in Plates
Ultrasonic guided waves (UGWs) have been proven to be an effective technique for structural health monitoring (SHM) due to their low attenuation and high sensitivity to defects across the entire structural cross-section, outperforming conventional inspection methods. However, the dispersive and multi-mode nature of UGWs presents significant challenges for structural inspection, limiting their effectiveness in damage detection and characterization. The interaction between different mode-frequency combinations and structural defects, such as delaminations and notches, affects UGW scattering according to complex mechanisms that challenge inspection techniques. Therefore, a deeper understanding of the distinct scattering phenomena with respect to defect types and sizes is essential for improving the diagnostics and prognostics of damage. In this study, numerical and experimental methods are employed to analyze UGW interactions with plate-like structures in both the time- and frequency- domains. First, the hybrid Global-Local method is used to simulate UGW propagation in isotropic and composite plates with defects of varying types and sizes. This enables a comprehensive investigation of the wave-damage interaction over a broad frequency range. Then, experimental testing is performed to observe and validate the numerical findings, using broadband guided wave testing. Signal processing is applied in the time and frequency domain to extract transfer and impulse response functions via the deconvolution method. The results from the numerical simulations reveal amplitude, time shifting and frequency content alterations that align with the experimental tests and that vary with defect type and severity, providing valuable insights for effective SHM applications. These studies demonstrate the effectiveness of the Global-Local approach in enabling quantitative SHM and prognostics by providing accurate and efficient predictions of UGW scattering responses. Furthermore, the distinct scattering behavior observed for different defect types and sizes highlight the potential of leveraging these features to solve the inverse problem of defect characterization.
Numerical and Theoretical Investigation of the Acoustoelastic Effect of Tensile and Shear Stress on the Propagation of Ultrasonic Lamb Waves in Plate
Initial stress affects the propagation of ultrasonic guided waves and therefore is relevant for the structural health monitoring field. Typically, only normal pre-stress is considered in the wave propagation. In this work, the effect of another relevant loading case, namely, shear stress, on the propagation of ultrasonic guided waves is investigated utilizing the finite element and semi-analytical finite element modeling while a stochastic metamodeling analysis is also presented. An aluminum plate is considered where the initial stress is set either as longitudinal tensile or shear stress along the propagating direction. Both methods show good agreement, revealing that for the fundamental Lamb wave modes and evaluated stress levels, the phase speed difference due to shear stress is less than due to tensile stress; however, speed changes quadratically with the shear stress level, unlike tensile stress which shows a linear behavior. Additionally, autoregressive and functional stochastic models are employed in view of a metamodeling approach to represent and assess stress effects on guided waves.
The Effect of Temperature on the PZT Sensor Response—Case Study with Guided Wave SHM
The guided wave structural health monitoring (GWSHM) systems can provide continuous monitoring of the critical engineering structures in several industries including energy and aerospace. Today’s digital tools, like numerical simulations, can provide rapid and extensive knowledge about GWSHM systems without the cost of implementation. On the other hand, the effect of environmental factors like temperature plays an important role in the GWSHM system response. To fully simulate the system, it is then mandatory to have reliable models for these factors. To this date, several research groups conducted studies to understand the effect of temperature variances on material properties; however, there is a lack of fast models for sensor response. This work focuses on the generalization of the pin-force PZT response model available in the software CIVA under varying temperatures (typ. from -20°C to 40°C). Measured impedance values were used to estimate the effective radius of the bonded PZTs. The numerical studies were validated experimentally on a case study without damage. By using proposed technique, the effect of temperature on the sensor definition, including bonding, is implemented in the numerical studies. Furthermore, this is an important step towards model assisted probability detection (MAPOD) of the systems under different environmental conditions.
Damage Indicator Selection Under Measurement Uncertainty for Detecting Selective Leaching in Metal Pipes Using Ultrasonic Wave Pitch-Catch
We develop a novel non-destructive evaluation (NDE) technique for detecting leaching damage using ultrasonic wave pitch-catch. This method combines guided wave propagation with advanced signal processing to detect leaching damage. In this method, ultrasonic guided waves travel from the actuator to sensor and interact with leaching damage on their path. The changes to the received wave signal due to leaching damage are quantified using three different damage indicators (DIs). Damage detection accuracy for these three DIs is investigated by conducting laboratory experiments on gray cast iron pipes. Leaching damage is induced in these pipes and piezoelectric transducers are attached to conduct ultrasonic wave pitch- catch and damage detection. For the three DIs, the most suitable DI that gives the highest sensitivity and specificity under measurement uncertainty is determined. Ultrasonic guided wave-based methods are found to be suitable for early detection of leaching damage detection.
Model-Assisted Damage Assessment in Composite Overwrapped Hydrogen Pressure Vessels Using Guided Waves
Maintaining the structural integrity of composite overwrapped hydrogen pressure vessels (COPVs) is increasingly critical for safe hydrogen storage and transportation, given the unique challenges posed by composite materials. Accurate structural assessment is vital for effective Structural Health Monitoring (SHM), and guided ultrasonic waves (GUW) show significant potential for this application. This study presents a model-assisted approach to damage assessment using guided wave-based SHM (GWSHM) on COPVs. By combining experimental data, numerical simulations, and machine learning techniques, we aim to enhance the reliability and accuracy of detection capabilities. Experiments were conducted on both intact vessels and those with artificial damage to generate baseline and damage-induced guided wave signals. Two simulation techniques were employed to support these measurements. Another key contribution of this work is the integration of model-based insights with data-driven approaches for damage assessment. The findings contribute to the advancement of GWSHM methodologies and support the safe, efficient deployment of COPVs in hydrogen storage applications.
Damage Localization Based on MUSIC Beamforming Algorithm Under Variable Temperature Conditions
Environmental temperature variations pose significant challenges in Lamb wavebased structural health monitoring by introducing amplitude and phase distortions that can mask damage-related signal features. This paper presents a novel damage imaging and localization method integrating an improved Multiple Signal Classification (MUSIC) algorithm with beamforming techniques to address these challenges. An array signal propagation model has been developed to explicitly account for temperatureinduced effects on wave propagation characteristics, while an enhanced MUSIC algorithm incorporating amplitude-phase error compensation through cost function optimization has been formulated to mitigate signal distortions. The method further employs beamforming-based spatial filtering to improve imaging resolution under varying temperature conditions. The effectiveness of the proposed method has been validated through extensive experimental studies conducted on aluminum plates with square hole damages under temperatures ranging from -40°C to 80°C. Results demonstrate that the method achieves consistent damage localization accuracy with relative errors maintained below 5.69% across all temperature conditions, while successfully preserving imaging resolution and boundary definition. The method's robust performance and computational efficiency make it particularly suitable for practical structural health monitoring applications where temperature variations are inevitable.
Offshore Wind Turbine Monopile Monitoring Using Guided Ultrasonic Waves
Following the rapid expansion of offshore wind farms (OWFs) within the next decade many existing OWFs, typically employing monopile supports, will come to the end of their design life. Life extension offers the potential for continued renewable electricity generation at competitive costs. The viability of continued operation can be quantified using fatigue reliability analysis, enabling risk-based decision-making for the life extension of OWFs. This contribution proposes a novel active learning framework with an ensemble of surrogate models, applied to the numerical model of an offshore wind turbine, to predict fatigue reliability. Guided waves can be employed to inspect and monitor inaccessible, submerged, and embedded sections of the wind turbine support, as they can propagate long distances along thin structures such as monopiles. This could allow for the detection of fatigue cracks and corrosion at critical weld locations below the mudline, providing important information for risk-based life extension. Experiments were conducted on a laboratory scale monopile to assess the guided wave propagation and sensitivity for the detection of critical defects. The guided wave data could be combined with wind and wave data to improve the fatigue reliability analysis based on Structural Health Monitoring (SHM).
High Accuracy Non-Contact Monitoring for Hole-Edge Crack of Aircraft Attachment Lug Based on Guided Wave Network
The attachment structure is the key component to ensure the structural integrity and bearing capacity in aircraft structure, and its service environment is often complex and harsh. As the main attachment structure between fuselage and wing, the aircraft attachment lug is much possible to initiates hole-edge crack at any direction and propagates, which leads to a decrease in structural integrity and then fracture failure. Owing to the advantages of non-contact monitoring, the hole-edge crack monitoring based on guided wave-structural health monitoring (GW-SHM) method has always been a topic of concern in recent years. However, research on aircraft attachment lugs still needs to be further developed. Due to the existence of large bolt hole and complex propagation characteristics of GW in aircraft attachment lug, it is difficult to accurately monitor hole-edge crack damage, especially small cracks. Aiming at this problem, a high accuracy monitoring method based on guided wave network is proposed in this paper. The damage features in GW network are quantified accurately by damage index (DI) method, and a reliable alarm mechanism is constructed. The crack localization imaging method is optimized for the bolt hole form to enhance the monitoring accuracy. The proposed method is verified by a monitoring experiment of small hole-edge crack in aircraft attachment lug. The results demonstrate that this method can achieve correct damage alarm and accurate damage location, which provides a research idea for the accurate monitoring of hole-edge crack damage.
Instantaneous Baseline Imaging Using Passive Ultrasonic Guided Waves from Fiber Bragg Grating (FBG) Sensors
The detection and quantification of damage plays a crucial role in several industries. Assessing their severity enables to schedule maintenance appropriately, optimizing costs and reducing risks. Thanks to their long range and high sensitivity to defects, ultrasonic guided waves allow for the Structural Health Monitoring (SHM) of various structures using only a small number of sensors. However, the use of traditional piezoelectric transducers requires extensive wiring installation that weighs on the SHM system’s costs and that may affect its reliability, and is limited to certain temperature and radiation ranges. To that extent, Fiber Bragg Grating (FBG) sensors offer an alternative instrumentation solution that withstands much higher temperatures and radiation levels, with a simplified installation thanks to the multiplexing of several sensors on the same optical fiber. They do not allow for actuation however, but passive guided waves techniques can be used to reconstruct active signals by exploiting the ambient excitation created by Environmental and Operational Conditions (EOCs). Furthermore, these EOCs and especially temperature have a significant impact on ultrasonic signals (independently of the instrumentation used, active or passive), which often exceeds that of the sought-after defects and thus limits the use of a baseline acquired from the structure in its pristine state. The instantaneous baseline technique addresses this issue by using damage free signals of the current state to create a statistical baseline, from which similar paths can be compared. In this study, passive ultrasonic signals acquired experimentally from FBG sensors are used for corrosion imaging through an instantaneous baseline technique. A thick steel plate, which is damaged locally using accelerated corrosion, is instrumented with multiplexed FBGs as well as piezoelectric sensors. Passive ultrasonic signals are acquired by both FBG and piezoelectric sensors using a compressed air jet as the excitation source, and active signals generated with the latter are also used for comparison. The instantaneous baseline method is applied to the signals and coupled with classical imaging techniques such as RAPID. Results obtained from the FBG passive and piezoelectric passive and active setups are then compared and discussed.
Three-Dimensional Imaging of Opaque Nanostructures in Silicon Chips Based on Ultrafast Optoacoustic Scanning Microscopy
The recent advances in micromanufacturing have been pushing boundaries of the new generation of semiconductor devices, which, in the meantime, brings new challenges in material and structural characterization. An ultrafast laser-enable optoacoustic characterization methodology is developed in this work, targeting in situ calibration and delineation of the three-dimensional (3-D), nanoscopic interior features of opaque semiconductor chips. With the guidance of ultrafast electron-phonon coupling effect and velocity-perturbated optical interference, a femtosecond-laser pump-probe set-up based on Sagnac interferometer is configured to generate and acquire picosecond ultrasonic bulk waves (P-UBWs) traversing the microchips. The interior features of the microchips shift the phase of acquired P-UBW signals, reflected in the perturbed probe laser beam. The phase shifts are calibrated to compute signal correlation of P-UBW signals between different acquiring positions, whereby delineating the interior features in an intuitive manner. The approach is experimentally validated by characterizing nanoscopic, invisible interior aurum (Au)-gratings with periodically varied depths in typical microchips. Results highlight that the 3-D nanoscopic features of the microchips can be revealed with a microscopic and a nanoscopic spatial resolution, respectively along the transverse and depth directions of the chip, where the Au-gratings become “visible” with a depth variance of a few tens of nanometers only. This proposed approach has provided a fast, nondestructive approach to “see” through an opaque microchip with a nanoscopic resolution.
Shape Derivative Adapted to Elastodynamics for the Reconstruction of Corrosion Defects
Today, the residual thickness of a structure is crucial information for many industries, including the nuclear sector. To this end Structural Health Monitoring (SHM) aims to establish continuous monitoring using sensors integrated into the structure to be inspected. We are particularly interested in tomographic methods using guided elastic waves. In the literature, the most widely studied guided elastic wave tomography methods are based on an acoustic propagation model. These methods are based on physical approximations, such as single-mode propagation or propagation in a waveguide-type structure, which allow a rapid estimation of the thickness. However, these assumptions are only valid if the wave packet of interest can be extracted from the rest of the signal and there is no mode conversion at the defects, as this would inevitably affect imaging resolution and potential application cases. We present here an adaptation of a method called ‘shape derivative’ that does not rely on these assumptions.
Thermo-Elastic-Plastic Behaviors of Vibro-Acoustically Modulated Interfacial Waves in Bolted Joint and Its Application in Monitoring of Early Bolt Loosening
The non-monotonic variation of nonlinear intensity of vibro-acoustic modulation (VAM) during the evolution of contact-type damage has been well observed. To enhance the detectability of modulated waves for bolt loosening, a method, which ingeniously makes use of the thermo-elastic-plastic susceptibility of the modulated waves under varying temperatures, is developed, referred to as thermally interfered vibro-acoustic modulation (TI-VAM). The level of the acoustic nonlinearity in the fully tightened joint can be enhanced under higher temperatures and that in low-stress preload can be enhanced under lower temperatures. The nonlinear response from bolt loosening dominates over material nonlinearity, ensuring the sensitivity of TI-VAM in practical applications. With such merit, this method can improve the monitoring of early bolt loosening or detecting other contact-type damage.
Frequency and Time Domain Analysis of Guided Waves for Damage Detection
Guided waves have been shown to be a very useful approach for damage detection. Their interaction with inhomogeneities, holes, cracks, delaminations or other defects present in the structure carries important information and can reveal the type and extent of the damage itself. Furthermore, depending on the nature and size of the damage to be monitored, an appropriate mode and wavelength can be used. Explicit algorithms are used to numerically simulate these interactions giving accurate predictions of what is obtained experimentally. However, the selection of the proper mode and wavelength to be used is strongly dependent on the nature and dimension of the defect and it often requires quite long computational times, as well as a tuning process that can identify the appropriate characteristic of the ultrasonic wave for each defect. Structures of a certain complexity and extent may contain defects of various nature and size and therefore a significant number of analyses may be required: depending on the specific problem, it is necessary to use more than one excitation pulse at difference frequencies and the time-domain analysis needs to be carried out more than once with a further increase of computational costs. Conversely, guided waves in the frequency domain can be used for the same class of problems and may offer some advantages in terms of computational time saving, especially in the case of a multi- damage problem for which a multi-frequency excitation is required. In this paper the numerical solution in terms of frequency response functions (FRF) of the guided waves problem is demonstrated to be an alternative and efficient approach to the time domain analysis. Moreover, the time domain solution can be reconstructed from the FRF data for any input frequency and waveform. The time domain solution reconstructed from the FRF data are compared to solution directly obtained in the time domain. Subsequently a damaged structure will be considered to demonstrate again the efficiency of the FRF approaches. Finally, the advantages and limitations of time and frequency approaches are discussed.
PSAFE: A Computationally Efficient Semi-Analytical Finite Element Scheme to Model Periodic Waveguides
We present a Semi-Analytical Finite Element (SAFE) formulation capable of han- dling structural systems with spatial periodicity along the wave propagation direction. This overcomes a fundamental limitation of existing SAFE schemes, which require translational invariance along the propagation direction. The key premise of our formu- lation, denoted PSAFE, is the assumption of a Bloch-form solution for the unknown displacement field within the waveguide. This transforms the problem into Fourier space, replacing the spatial coordinates with their Fourier series expansion counterparts. As a result, we obtain an infinite set of coupled equations governing the waveguide’s dispersion relations, each corresponding to a specific Fourier coefficient. To solve this problem numerically, we truncate the infinite series to a finite number of terms and reformulate the system as a linear eigenvalue problem. We demonstrate that accurate results can be obtained even with low truncation orders and that increasing the system’s dimensionality improves accuracy, albeit at the cost of higher computational expense.
Component Level Uncertainty Quantification of Thickness Estimations Computed from Full-Field Ultrasonic Inspection
This work performs a case study on the error and uncertainty of thickness estimations derived from acoustic steady-state excitation spatial spectroscopy (ASSESS) – a full-field ultrasonic inspection technique that can be utilized for structural health monitoring applications. Thickness estimation was performed using a 40 kHz excitation frequency on 0.2 m x 0.2 m steel plates ranging in thickness between 1.55 mm and 19.14 mm. The experimental thickness estimation results were compared to corresponding analytical results and thickness errors resulting from experimental uncertainties were investigated.
Beamforming Imaging in Cylindrical Structures Using Helicoidal Reflected SH Waves
Implementing guided wave imaging (GWI) for structural health monitoring (SHM) of cylindrical structures can be enhanced by simplifying the physics and utilizing all the information generated by wave interactions with the mechanical guide and discontinuities. A beamforming method for SH0 waves reflected from a trough-hole in a cylinder has been developed. This method considers the mode conversion from T(0,1) to SH0 waves, which follow direct and helical paths before converting back to torsional waves. The use of helical paths enables the deployment of an array of sensors distributed along a section of the cylinder’s circumference. Indeed, signal identification and integration into a beamforming scheme present challenges due to the multiple trajectories that prop- agate around the cylinder. The objectives of the numerical analysis are threefold: first, to model the propagation and interaction of torsional waves with a discontinuity; second, to numerically identify the helicoidal wave trajectories for full rotations (orders); and third, to develop and implement a frequency-domain MUSIC beamforming algorithm adapted for cylindrical structures. The results demonstrate the ability to reconstruct the defect’s position and orientation. These findings highlight the potential of the proposed method for accurate defect localization in cylindrical structures.
Identification of Trapped Foreign Objects in Railway Switches Using Drive Outputs
Railway switches are crucial in enhancing railway network flexibility but are also a significant source of operational disruptions. A common cause of disruption is when foreign objects such as ballast stones, metal debris, snow, or ice become trapped between the switch and stock rail during switch rail actuations. Such obstructions can hinder switch operations and thereby cause train delays and increased operational costs. If they cause significant local gauge narrowing, there is an increased risk of derailment if trains pass by. To prevent such scenarios, it is crucial to accurately and cost-effectively identify trapped foreign objects at an early stage. The Swedish switch designs currently in operation often use a combination of drive (point machine) locking tolerances and additional position sensors along the switch rail to detect foreign objects that can constitute a derailment risk. These sensors require maintenance and sometimes cause traffic disruptions by themselves due to malfunction. When an obstruction is present, the drive exerts additional force to close the induced gap via bending of the switch rail, resulting in increased power and current consumption. The aim is to develop a robust condition monitoring system for identifying trapped foreign objects by analysing the current output of drives in addition to their locking tolerances. By examining current output curves, deviations from ideal operational conditions can be observed, enabling the identification of dangerous objects. The aim of this paper is to analyse deviations from ideal operational conditions for different object scenarios by utilizing a multibody simulation (MBS) model of a standard switch. Simulations were carried out for varying object sizes and positions between the drives. The presence of a trapped foreign object under various operational conditions was detected by analyzing the force-displacement outputs at the drives.
Trackside Installation System for Rail Neutral Temperature Estimation
This short article describes one of the latest advancements of a monitoring/inspection technique for the estimation of localized longitudinal stress in continuous welded rails (CWR). The technique is based on the use of vibration measurements and machine learning (ML). A finite element analysis is conducted to model the relationship between the boundary conditions and the longitudinal stress of any given CWR to the vibration characteristics of the rail. The results of the numerical analysis are used to train a ML algorithm that is then tested using field data obtained by an array of accelerometers polled on the track of interest. The proposed technique was tested in the field. A commercial FEM software was used to model the rail track as a short rail segment repeated indefinitely and under varying boundary conditions and stress. A ML model was developed to infer the rail neutral temperature and the local resistance of rails to vertical and lateral displacement. The results of the experiments demonstrated that the success of the technique is dependent on the accuracy of the model and the ability to properly label the modes of the detected frequencies. This study builds upon previous research conducted at the University of Pittsburgh and the interested reader is referred to previous publications from the authors for more details about the proposed technique.
Improving the Capabilities of Ultrasonic Inspection of Railroad Rails in High-Speed and Walking-Speed Testing
Ultrasonic testing is widely used to probe railroad rails for internal defects. Rails are continuously inspected by hi-rail vehicles operating ultrasonic Rolling Search Units (RSUs) at speeds up to ~30 mph. In addition, flaw detection is usually followed by flaw verification using a hand-held ultrasonic probe to determine the flaw size and orientation. Ongoing research in many laboratories worldwide is aimed at improving the performance of ultrasonic rail inspections in terms of practicality and reliability of defect detection. This paper will present recent advances on two topics: (1) achieving rail inspection at high speed by “smart trains”, and (2) improving ultrasonic flaw imaging at walking speed. These efforts are being pursued at University of California San Diego (UCSD) with the collaboration of the U.S. Federal Railroad Administration (FRA) and MxV Rail, Inc. On the first topic, the latest version of a non-contact ultrasonic testing prototype will be presented to allow flaw detection at revenue speeds. Once fully developed, this capability would allow trains to perform inspections during normal operations (“smart train”), hence minimizing traffic disruptions and exploiting the redundancy of multiple train passes over the same segment of rail. Results from field tests conducted in collaboration with MxV Rail, Inc. will be presented in terms of Receiver Operating Characteristic (ROC) curves assessing the trade-off between Probability of Detection (POD) and Probability of False Alarm (PFA). On the second topic, research is being conducted to implement ultrasonic Synthetic Aperture Focus (SAF) in rail flaw imaging using a transducer array hosted in an RSU wheel. Specifically, an RSU imaging prototype is being developed based on recent advances in SAF imaging, including sparse firing, multi-mode detection and eigen-filter analysis. The latest feature allows eliminating artifacts affecting RSU rail imaging (e.g., reflections from the wheel-rail interface and from the railhead bottom flange) so as to successfully isolate the target flaws. Results will be shown during scanning of a sample rail segment in the laboratory, motivating expanding the idea for defect imaging in walking speed.
Automated Highway Pavement Management Systems: From Inspection to Maintenance
Highway pavement assets require intensive management due to their large scale and deteriorating nature. Traditional pavement management depends on inspections using dedicated sensors and vehicles, which are both infrequent and costly. Moreover, maintenance activities face unavoidable constraints because they rely on human labor. In this study, we explore the potential of automation in two key steps over the highway pavement management cycle: inspection and maintenance. First, we propose an automated pavement monitoring system that utilizes non-dedicated vehicle sensors, including accelerometers and dashcam cameras. Pavement condition data collected by dedicated inspection vehicles are utilized as ground truth for machine learning processes. Beyond assessing current conditions, we estimate pavement deterioration models using collected panel data. These models enable service life predictions and support optimal asset management strategies to minimize expected life cycle costs over long-term planning horizons. Second, we discuss the benefits of automated pavement maintenance technologies with AI applications, in the context of life cycle analysis.
Stability Analysis and Field Test of High-Speed Maglev Vehicle-Guideway System
Track irregularities are a major excitation source in maglev transportation systems, and controlling these irregularities is key to ensuring long-term operational comfort. Variations in train speed alter the excitation frequency induced by track irregularities, often leading to amplified vibrations within specific speed ranges during variable- speed operation. This phenomenon negatively impacts ride comfort. This study conducted field tests on the stability of a high-speed maglev system. We investigate the vibration response characteristics of a high-speed maglev train during variable- speed operation and analyzed the stability trends of the system. A high-speed maglev vehicle-guideway coupled vibration model was developed based on parameters from the Shanghai Maglev Demonstration Line. The study investigated the dynamic behavior of key indicators, including carriage acceleration, suspension frame acceleration, and air gap, during acceleration phase, identifying the speed ranges in which vibration amplification occurs. Furthermore, the effects of key track irregularity parameters were examined by modifying the wavelength and amplitude of irregularities. The study identified the critical wavelengths that significantly influence dynamic responses under variable-speed conditions. Based on the findings, we propose the wavelength range and amplitude thresholds that should be closely monitored and controlled in acceleration zones. This research provides a theoretical basis for managing track irregularities in variable-speed sections and offers guidance for maintaining the long-term ride comfort of maglev transportation systems.
A Review of International Guidelines for Railroad Bridges Focusing on Fatigue Assessment and Structural Health Monitoring
Most steel bridges designed between the early 19th century and the mid-20th century were built of mild steel plates and profiles with riveted joints. The assessment of existing railroad bridges has become increasingly important in railroad infrastructure management. Many riveted bridge structures have exceeded their original design lifespan, requiring assessment to ensure structural safety and reliability requirements. The remaining fatigue resistance is often a dominant factor during assessment. Since historical construction methods, such as riveting instead of welding, differ from modern techniques, infrastructure operators seek tailored solutions for evaluating these structures. Structural Health Monitoring (SHM) is gaining prominence as a tool for enhancing assessment accuracy and supporting decision-making. There are already various guidelines from railroad operators available. However, the integration of SHM in these guidelines is very different. Therefore, this study compares different assessing guidelines for existing railroad bridges, focusing on SHM integration and fatigue assessment for riveted steel bridges. The guidelines analyzed indepth include “DB InfraGO AG. Handbuch 80500 – Tragsicherheit bestehender Eisenbahnbrücken“ (DB InfraGO AG. Manual 80500 – Structural safety of existing railroad bridges), “ÖNORM B 4008-2. Bewertung der Tragfahigkeit bestehender Tragwerke. Teil 2: Brückenbau“ (OENORM B 4008-2. Assessment of the load-bearing capacity of existing structures. Part 2: Bridge construction), and “Berliner Verkehrsbetriebe. Richtlinie zur Bewertung der Tngenieurbauwerke im Bereich der U-Bahnanlagen der Berliner Verkehrsbetriebe“ (Berlin transport company. Guideline for the assessment of engineering structures in the subway system of the Berlin transport company), additionally a US guideline will partly be considered. The comparison identifies differences, similarities, and gaps in current reassessment methodologies, particularly in fatigue assessment approaches and SHM applications. These results may form the basis for the development of harmonized recommendations for assessment of existing riveted railroad bridges.
Structural Displacement Estimation of Rail Bridges Through Millimeter-Wave Radar, Accelerometers, and Non-Dedicated Multi-Modal Sensing
Rail bridges are essential components of modern transportation networks but often operate beyond their intended service life, leading to growing concerns about structural integrity and public safety. Traditional structural health monitoring (SHM) systems can be prohibitively expensive and complex, limiting their widespread use— especially for smaller rail bridges with constrained budgets. To address these challenges, we present a novel, cost-effective radar–accelerometer fusion sensor designed to provide real-time, high-precision displacement and acceleration measurements. Our approach integrates a millimeter-wave frequency-modulated continuous-wave (FMCW) radar module with a three-axis MEMS accelerometer, enabling accurate detection of even the slightest structural movements. Specifically, our sensor achieves a root-mean-square error for displacement below 0.1 mm, which is suitable for capturing subtle deformations that may indicate underlying structural issues. The hardware leverages off-the-shelf components, including an Infineon 60 GHz radar chipset and a microcontroller equipped with wireless interfaces (2.4/5.0 GHz Wi-Fi, Bluetooth, and Gigabit Ethernet), all housed in a compact (100 × 80 × 50 mm³) enclosure. Due to its streamlined design and off-the-shelf parts, mass production costs can be kept under $1,000, making it feasible for large-scale deployments. To enhance measurement fidelity, the sensor employs automatic radar target selection, guided by initial radar and acceleration data, to overcome multiple reflections and phase ambiguity in FMCW signals. This ensures robust performance under a range of field conditions and traffic scenarios. Beyond local displacement and acceleration measurements, our framework incorporates existing fiber-optic cables— originally installed for telecommunications—to form wide-area strain gauges. By using distributed fiber-optic sensing, rail bridge operators gain a broader, continuous view of structural behavior across extensive spans without requiring further dedicated sensor installations. These data sources are synchronized with a common NTP server, enabling advanced analytics that fuse displacement, strain, and vibration information into a unified, real-time rail bridge monitoring platform. Through this multi-modal integration, our radar–accelerometer fusion sensor not only addresses critical cost and scalability concerns but also provides a transformative leap in monitoring accuracy and operational efficiency. By integrating cost-effective hardware and distributed fiber-optic sensing, rail bridge stakeholders can pursue more precise, data-driven maintenance strategies, leveraging highly accurate displacement and acceleration measurements to proactively address structural concerns and extend service life
Employing LEWIS1 Accelerometers on the Rail Runner for Strain Estimation
Structural Health Monitoring (SHM) plays a vital role in ensuring the safety of railroad infrastructure by enabling the detection of damage, fatigue, and potential failure. While strain is a direct indicator of structural stress and deformation, measuring it reliably across large-scale railroad systems presents significant challenges due to environmental exposure, logistical difficulties in sensor placement, and the limitations of traditional strain gauges. This paper presents an alternative SHM methodology that leverages acceleration data collected from Lightweight Efficient Wireless Intelligent Sensor (LEWIS1) accelerometers, which are low-cost, rapidly deployable, and highly adaptable sensors. The LEWIS1 sensors were deployed aboard the Rail runner train in New Mexico to capture acceleration data during various events such as deceleration, acceleration, steady-state movement, and bridge crossings. By analyzing both time-domain and frequency-domain characteristics, researchers were able to identify distinct time-domain event trends and modal resonances, which form the foundation for future strain estimation using modal decomposition and expansion techniques. This approach addresses the scalability, cost, and robustness issues faced by traditional strain measurement methods and offers a pathway to strain estimation using acceleration-based SHM. Future work will involve integrating these acceleration measurements with a finite element model of the train to estimate global strain distributions throughout train structures and bridge components.
Limitations and Future Direction for Structural Health Monitoring of Railroad Bridges Subjected to Over-Height Vehicle Impacts
Low clearance through-plate girder railroad bridges in the United States are highly susceptible to impacts from over-height vehicles, which can lead to structural damage and disruptions in railroad bridge service. Current post-impact inspection protocols require bridge closures, causing unnecessary delays when minor impacts occur. This study presents a low-cost structural health monitoring-oriented framework for automated impact detection, severity classification, and condition assessment using wireless smart sensors. The proposed structural health monitoring framework leverages machine learning models to detect and characterize impact events and estimate resulting residual displacements using acceleration data alone. Although this framework demonstrates promising performance in both numerical and field settings, some limitations remain. The current approach has been validated only for single-span through-plate girder bridges, relies solely on acceleration data, and does not incorporate vehicle speed or visual confirmation. Future directions include integrating additional sensing modalities such as cameras for event verification and visual assessment, expanding applicability to multi-span or alternative bridge types, and incorporating traffic history and near- miss data to support predictive impact risk modeling. These extensions will further enhance the utility, scalability, and resilience of automated post-impact response strategies for railroad infrastructure.
Estimating Rail Neutral Temperature using Local Resonance and Probabilistic Machine Learning
The continuous welded rail (CWR) has been widely used in modern railways due to their support to high transport speed while requiring less maintenance compared with jointed track. However, CWR is susceptible to internal stress because of restrained free thermal expansion and contraction in its axial direction. During summer, the excessive heat on the rail will cause expansion of the rail, but it is constrained, leading to a huge axial force. If the axial force is excessively large, thermal buckling could be triggered depending on track conditions. In fact, the sense and magnitude of the built-up rail axial stress depend on the rail temperature relative to the set rail neutral temperature (RNT). Estimating RNT without taking baseline measurements has been a long-standing challenge for the railway engineering community. In this study, we present the most recent advancement in RNT estimation using intrinsic local resonances in rails. This study demonstrates that frequencies of these local resonances, including zero-group velocity and cutoff frequency resonances whose energy are trapped locally close to the load zone, are insensitive to the presence of rail supports and sensitive to rail temperature and axial loads, which has great potential for RNT estimation. The data collection system can consistently extract rail local resonances from a fully instrumented revenue-service site, where strain gauges and thermocouples were attached and calibrated during the track construction and can supply the ground truth of RNT and rail thermal forces. Probabilistic machine learning algorithms are developed to predict RNT of the revenue-service site using rail temperature and frequencies of rail local resonances, both of which are directly measurable. The performance of the proposed machine learning framework is evaluated by comparing the predictions with the ground truth.
2D Laser-Induced Phased Arrays for Large-Area Scans Using Poisson-Disk Layouts
There is an increased need for the development of reliable non-contact non- destructive evaluation (NDE) techniques which can be deployed using machines/robots in remote locations and risk-associated environments. This work suggests a new ultrasound scanning technique for large areas using 2D laser-induced phased arrays (LIPAs). The objective is to achieve a given detection sensitivity using the smallest possible number of measurements. This reduces the scanning time, size of the collected data, and energy usage. Bridson Poisson sampling is proposed to define both the generation-laser aperture relative to a single detection point (physical aperture) and the detection-laser scanning points over an arbitrary inspection surface. The proposed imaging solution is based on the total focusing method (TFM) but differs from conventional and previous LIPA solutions. The full matrix capture (FMC) for each imaging location on the surface, referred to as the region of interest (ROI), is determined by the available detection points within its vicinity. This vicinity is expressed by a specific radius, defining the size of the computational aperture. The proposed methodology is analytically evaluated using ray-tracing model simulations. Poisson- disk sampling is compared to regular-shaped (square and triangular) grids, demonstrating its clear superiority in both detection/imaging performance and scanning time. Recommendations for optimisation and experimental validation are suggested for future work.
Indirect Bridge Monitoring from Instrumented Vehicles
This paper investigates the sensitivity of a rotation-based damage estimation method applied to simply supported beams under a quasi-static moving load. The method estimates three damage parameters, damage start location (L1), end location (L2), and severity (β), using rotation observed at the load location. In this study, results from finite element model (FEM) simulations of three damage scenarios characterize how these damage parameters affect the rotation response. For each case, two parameters are fixed to their best estimated values while the third is varied across its top 100 values. The resulting sensitivity, i.e. change in rotation with respect to each parameter, illustrates rotation changes with respect to the moving load location and parameter variation. This establishes the sensitivity of the method and motivates the use of rotation-based damage detection methodologies.
AI-Enabled Road Condition Monitoring: A Scalable and Affordable Approach
The condition of U.S. road infrastructure continues to deteriorate, with over 39% of public roadways rated in poor or mediocre condition and an annual economic impact exceeding $160 billion. Traditional visual inspection methods are slow, laborintensive, and often subjective, while high-end survey vehicles and smartphone-based alternatives either lack scalability or measurement reliability. To address this gap, we present a scalable, AI-assisted approach for automated pavement condition monitoring using visual data collected from city-wide deployments. The proposed framework detects surface defects and assigns standardized condition scores aligned with industryaccepted rating systems. Field testing in two Indiana cities—West Lafayette and Fort Wayne—spanned over 2,000 miles and resulted in more than 13 million geo-referenced image captures. Results demonstrate the system’s potential to support timely, consistent, and cost-effective evaluations of roadway health. This work highlights the importance of integrating AI with data-driven inspection practices to improve the sustainability and efficiency of municipal pavement management.
Permanent Magnet Electromagnetic Hybrid Suspension System for Medium and Low-Speed Maglev Train
The medium and low-speed maglev trains adopt EMS suspension technology. Through statistical analysis of the energy consumption data of the commercial operation line Beijing S1 maglev train, the data shows that the energy consumption of the suspension system is as high as 20%. To meet the development requirements of green, low-carbon, and environmental protection, and reduce suspension energy consumption, research on permanent magnet electromagnetic hybrid suspension technology is carried out. Firstly, the design, structural optimization, and model establishment of the hybrid electromagnetic circuit scheme were completed. Secondly, the centralized suspension control technology based on the two in one concept has been overcome, and the suspension control algorithm based on the modular decoupling control concept has been studied. A permanent magnet anti suction control strategy and fault-tolerant control algorithm for the suspension control system suitable for medium and low-speed maglev trains have been proposed. Finally, the fabrication of a permanent magnet electromagnetic hybrid suspension system was carried out, and experimental verification was conducted on suspension energy consumption, temperature rise, and operational performance for the permanent magnet electromagnetic hybrid suspension system. The experimental results indicate that the permanent magnet electromagnetic hybrid suspension system scheme is feasible; Compared to EMS systems, under rated load, the maximum temperature of the 24-hour static suspension electromagnet is controlled at 56 ℃, reducing suspension energy consumption by 63.6%. This is a green, low-carbon, and energy-saving technical solution. This technical solution improves the adaptability of the suspension system and promotes the development of medium and low-speed maglev trains.
Analysis of Unintended Hydrogen Releases from a Fuel Cell Tram
Hydrogen is a promising alternative energy carrier, and its application in the industrial field, especially in the transportation sector, is increasing through fuel cells to power vehicles. Hydrogen fuel cell vehicles are typically equipped with high-pressure on- board storage tanks. Due to the low volumetric energy density of hydrogen, these tanks are relatively large to provide a comparable range to current fossil fuel vehicles. Therefore, hydrogen is also suitable for large and heavy vehicles, as they have more space than passenger cars. However, accidental hydrogen leakage from high-pressure containers can have serious consequences. Therefore, in-depth research on hydrogen safety issues is necessary to promote the popularization and commercialization of hydrogen energy. CRRC plans to use hydrogen to power urban rail transit systems to reduce the demand for urban electricity supply. This study modeled hydrogen leakage and diffusion in the scenario where the on-board storage tank of a fuel cell tram is placed inside the carriage, including both stationary and moving scenarios. The results show that upward leakage will form a flammable cloud above the tram roof, and when hydrogen diffuses inside the carriage, the hydrogen concentration in the front part of the carriage is higher than that in the rear part. Compared to the stationary situation, vehicle movement will accelerate the diffusion of hydrogen from inside to outside the carriage and significantly reduce the mass of the flammable cloud formed. Additionally, the volume of the flammable cloud will decrease as the vehicle speed increases. This work provides guidance for the placement of hydrogen storage tanks and the structural design of fuel cell trams.
Screen-Printed Carbon Nanotube Polymer Composites for Impact Sensing in Electric Vehicle Batteries
Under-floor impacts are a major cause of thermal-runaway in EV battery packs, yet existing metal or MEMS sensors are heavy, costly, and cover only limited areas, leaving significant portions of the battery unmonitored. We introduce an ultralight, flexible, screen-printed carbon-nanotube (CNT)/epoxy patch sensor that detects impacts through piezoresistive resistance shifts. Conductivity increases with CNT loading, but impact sensitivity peaks at an intermediate CNT fraction and smaller print diameters, defining an optimal window of CNT content, print passes, and sensor footprint. Mounted on a prismatic 8 kWh module, the patches detected impacts ranging from 5–20 J within 20 ms, showcasing over tenfold improvement in strain sensitivity compared to conventional sensors, with negligible added mass. Furthermore, these sensors offer exceptional flexibility and adaptability, enabling their application across various complex battery pack geometries. These results demonstrate a scalable, practical route to comprehensive pack-level impact monitoring. Ongoing research will extend detection capabilities to impacts below 5 J and integrate artificial intelligence (AI) algorithms for mapping resistance signals to impact energy, precise location, and damage severity, paving the way for self-reporting battery-management systems capable of proactively preventing catastrophic failures. This advancement significantly enhances the safety, reliability, and operational efficiency of electric vehicles, promoting broader adoption and consumer confidence in EV technologies.
Tracking Soil Saturation in Earthen Embankments Using a Network of Wireless Conductivity Sensors
Expansive clay embankments along transportation corridors are vulnerable to moisturedriven shrink-swell cycles that threaten their structural integrity. This work presents a low-cost, solar-powered, wireless sensor network designed to monitor soil moisture in real-time using electrical conductivity as a proxy. Five custom-built sensors, deployed 10 cm below the surface, record conductivity, temperature, humidity, and barometric pressure, transmitting data to a solar-powered base station that syncs with a cloud server for remote access. Field testing over seven days validated the system’s durability, communication reliability, and power sustainability under variable weather conditions. Spatial moisture maps generated using spatial interpolation demonstrated the system’s utility in identifying zones of elevated saturation. This open-source system represents a scalable tool for continuous monitoring of earthen embankments to support early warning and risk mitigation efforts.
Monitoring Bridge Displacement Using Pre-Existing Telecommunication Fiber
Monitoring the condition and performance of bridges is critical for enhancing the resilience and safety of urban infrastructure. Sensor-based approaches (both mobile and in-situ) have been developed for monitoring bridges; however, such systems are limited in temporal information, or costly to install and maintain, and thus not scalable. Our previous works have demonstrated that ubiquitous pre-existing telecommunication fiber optic cables can be turned into a dense array of vibration sensors using distributed acoustic sensing (DAS). This enables scalable, high spatial resolution (meter-level) bridge health monitoring, as a single DAS interrogator can turn up to 100 km of telecommunication fiber into distributed strain sensors. While only the dynamic (1-10 Hz for short to medium-span bridges) response of the bridge using telecommunication fiber has been studied before, quasi-static (0.01 - 1 Hz) response of the bridge is critical for BHM tasks such as load rating and serviceability assessment. To this end, we aim to estimate quasistatic displacement response of bridge from telecommunication fiber response. The key challenge is the coupling between the fiber and the bridge that distorts the bridge response measurements. The telecommunication fiber optic cables are often laid inside conduits, which are attached to bridges. As the bridge vibration passes through these attachments, it gets distorted. To address this, we characterize the vehicle-induced quasistatic strain measured by telecommunication fiber, examining its response under different load positions and magnitudes. Based on this characterization, we model the transfer function as a linear relationship between DAS measurements and actual bridge response and infer the parameters using a reference sensor installed for calibration. Our evaluation results from two real-world in-service highway bridges demonstrate that DAS can serve as a displacement sensor with sub-millimeter accuracy.
Vehicle Speed Invariant Drive-By Bridge Damage Detection
Given the central role of bridges in transportation networks, continuous monitoring of these structures is crucial to detect damage, ensure long-term serviceability, and prevent catastrophic failures. Traditional inspection methods, however, are costly, laborintensive, and often subjective. Sensor-based approaches, such as strain gauges or accelerometers installed directly on bridges, require significant installation and maintenance efforts, limiting their scalability. An emerging alternative leverages vehicle–bridge interaction: by analyzing dynamic responses recorded by in-vehicle sensors, bridge conditions can be assessed without installing dedicated instrumentation on the structure. Vehicle accelerations reflect bridge-induced vibrations during crossings and indirectly encode dynamic properties of the bridge. However, variability in vehicle speed poses a significant challenge, as it affects the vibration signatures captured by accelerometers while remaining independent of bridge health. This confounding factor hinders the extraction of reliable, damage-sensitive features. To overcome this challenge, we propose a vehicle speed invariant drive-by bridge damage detection model that employs adversarial learning to extract features from vehicleinduced vibrations that are sensitive to structural damage while invariant to vehicle speed. The model integrates long short-term memory (LSTM) layers with a Gradient Reversal Layer (GRL). The LSTM layers capture temporal patterns in the frequency components, enabling the extraction of features that reflect subtle temporal variations across the full vibration spectrum. The GRL imposes speed invariance by adversarially optimizing the feature representation to maximize accuracy in damage classification while minimizing performance in speed prediction. We evaluate our method through a lab-scale experimental vehicle–bridge system with the vehicle running at varying speeds. Our model performs 1.4× better than baseline methods for bridge damage detection, achieving an accuracy of 91.01% and an F1-score of 93.29%.
Transformer-Based Indirect Structural Health Monitoring of Rail Infrastructure with Attention-Driven Detection and Localization of Transient Defects
Indirect structural health monitoring (iSHM) for broken rail detection using onboard sensors presents a cost-effective paradigm for railway track assessment, yet reliably detecting small, transient anomalies (2-10 cm) remains a significant challenge due to complex vehicle dynamics, signal noise, and the scarcity of labeled data limiting supervised approaches. This study addresses these issues through unsupervised deep learning. We introduce an incremental synthetic data benchmark designed to systematically evaluate model robustness against progressively complex challenges like speed variations, multichannel inputs, and realistic noise patterns encountered in iSHM. Using this benchmark, we evaluate several established unsupervised models alongside our proposed Attention- Focused Transformer. Our model employs a self-attention mechanism, trained via reconstruction but innovatively deriving anomaly scores primarily from deviations in learned attention weights, aiming for both effectiveness and computational efficiency. Benchmarking results reveal that while transformer-based models generally outperform others, all tested models exhibit significant vulnerability to high-frequency localized noise, identifying this as a critical bottleneck for practical deployment. Notably, our proposed model achieves accuracy comparable to the state-of-the-art solution while demonstrating better inference speed. This highlights the crucial need for enhanced noise robustness in future iSHM models and positions our more efficient attention-based approach as a promising foundation for developing practical onboard anomaly detection systems.
Displacement Estimation of Reactor Containment Buildings Using Accelerometer and FMCW Millimeter-Wave Radar
This study presents an advanced fusion-based sensing methodology designed specifically for precise displacement and acceleration monitoring of Reactor Containment Buildings (RCBs). Reliable measurement of structural displacement in RCBs is critical for ensuring structural integrity and operational safety. To address challenges associated with traditional displacement monitoring techniques, this research integrates data from a three-axis MEMS accelerometer and phase-shift measurements obtained from millimeter-wave frequency-modulated continuous-wave (FMCW) radar. By fusing these distinct sensor modalities, we significantly enhance measurement accuracy, achieving displacement estimation resolutions superior to 0.1 mm for both static and dynamic structural responses. The proposed sensor's hardware architecture emphasizes cost-efficiency, compactness, and reliability. The sensor system employs an Infineon 60 GHz radar chipset, combined with a MEMS accelerometer and a microcontroller supporting multiple wireless communication protocols (2.4/5.0 GHz Wi-Fi, Bluetooth, and Gigabit Ethernet). This robust yet economical sensor design results in a compact device (100 × 80 × 50 mm³) and is projected to cost under $1,000 per unit when mass-produced, making it highly suitable for wide-scale deployments in nuclear facilities. A key innovation of the developed fusion sensor is its automated besttarget selection capability, which utilizes initial short-duration radar and accelerometer readings to efficiently determine the most stable structural reflections and establish accurate conversion factors between line-of-sight displacement and actual structural displacement. Additionally, the accelerometer data aids in addressing the radar phasewrapping issue through an adaptive phase-unwrapping algorithm, thus significantly enhancing measurement reliability and accuracy. To further improve displacement estimation, radar-derived displacement data is combined with accelerometer measurements using a finite impulse response (FIR) filter, which mitigates noise and drift typically observed in acceleration-based displacement estimations. Through experimental validations conducted on structural models, this sensor fusion method demonstrated improved displacement measurement accuracy with errors consistently below 1 mm. Given its capacity for automatic calibration, effective target selection, and enhanced resilience against occlusions and environmental interferences, the sensor offers a reliable solution for continuous monitoring of RCBs. Ultimately, the proposed sensor fusion approach presents significant potential for adoption in nuclear power facilities, enhancing predictive maintenance, optimizing structural health management strategies, and reinforcing overall operational safety.
Interpretable Deep Learning for Railroad Health Monitoring Using Accelerometers and Distributed Acoustic Sensing
The health monitoring of long-distance infrastructures, such as railways, poses significant challenges due to extensive spatial coverage, operational complexity, and varying environmental conditions. Traditional methods are often limited by high costs and insufficient spatial resolution, making them unsuitable for large-scale and dynamic scenarios. Distributed sensing systems offer a promising alternative, enabling real-time, cost-effective monitoring over long distances. This study proposes an integrated system that combines accelerometers with Distributed Acoustic Sensing (DAS) to address these challenges. The system has been deployed on a subway line in Singapore for continuous monitoring under operational conditions. To overcome issues with DAS signal accuracy and physical interpretability, an interpretable deep learning framework is developed, fusing DAS and accelerometer data. This framework incorporates state-of-the-art interpretability techniques to enhance prediction accuracy and provide insight into model decision-making. As a case study, the proposed method is applied to detect rail fastener failures—common defects in railway systems. Field experiments using hammer impact excitation were conducted, and the resulting vibration responses were recorded by the integrated sensing system. These data were used to train and validate the proposed model. Results demonstrate that the framework outperforms traditional DAS-only approaches in both accuracy and generalization, leveraging interpretable features that align with known rail dynamic behavior. These findings underscore the method’s potential for scalable, reliable, and transparent structural health monitoring in railway infrastructure.
Human-Structure Interaction in Stadiums: Statistical Analysis of Vibration Comfort Levels Variability
Crowd-induced vibrations in stadiums pose challenges to structural integrity, human comfort, and long-term reliability. This paper presents a methodology that integrates ISO-weighted filtering, modal analysis, and statistical distribution modeling to evaluate vibration variability across three seating zones during a football game. Vertical vibrations are collected in the band, lower student, and upper student sections, which were processed to extract the vibration dose value (VDV). Modal peaks at 2.34 and 4.69 Hz were consistent in sections, with an additional 13.18 Hz mode observed in the band zone. Statistical fitting of Normal, Lognormal, and Weibull distributions to the VDV data yielded exceedance probabilities of 10.9%, 20.5 %, and 29.8 % for the band, lower student, and upper student sections, respectively. These results underscore the notable differences in comfort limits variations under identical game conditions. Future work recommendations are extended to apply the approach to multiple games, investigate causal links between variability and crowd-related factors, and quantify the contribution of each section to general discomfort to inform targeted design and maintenance strategies. The predictive capability of the framework offers a valuable tool for stadium monitoring and design optimization.
Using Interpretable Machine Learning for Analyzing Acoustic Startle Response in Rodents from Acoustic Emissions Data
Rodent behavior studies in neuroscience and psychology investigate various sensory and motor responses. One such study examines the acoustic startle response (ASR), an unconditional reflex to an unexpected and intense acoustic stimulus characterized by a rapid contraction of facial and skeletal muscles. These contractions generate subtle mechanical forces that propagate through the surface that the animal is in contact with, resulting in acoustic emission (AE) signals. The AE signals travel through the plate as guided ultrasonic waves, carrying information about the animal's motor response to ASR and its physiology, behavior, and underlying psychological state. Various ASR experiments were conducted to assess animal’s reactions to sudden auditory stimuli. The experiments involved three strains of mice (129S6/SvEv, CBA/CaJ, and C57BL/6J) in an open field equipped with AE sensors. The AE data were collected as discrete wavepackets (AE hits) using an amplitude threshold-based acquisition system, with each hit containing signal features that characterize the corresponding movement or behavior. We propose the use of the Explainable Boosting Machine (EBM) to analyze AE data generated by the animal during movement. Our goal is to distinguish ASR-related AE hits from normal AE activity, aiming to analyze the characteristics of each hit to confirm its association with ASR events. This classification is crucial not just for the detection of ASR occurrences but for analyzing the intensity and variability of startle responses across different conditions and strains. The dataset consists of AE-derived features (e.g., amplitude and frequency) combined with biometric factors (e.g., weight, sex) and environmental variables (e.g., lighting conditions, stress exposure). By leveraging EBM’s interpretability, this study identifies key factors influencing ASR and provides a transparent, data-driven approach for analyzing ASR in rodents. EBM provides an effective means to understand complex animal-structure interaction and to analyze the dynamic interplay between body movements and surrounding structures, revealing their mutual influences and shedding light on intricate biomechanical patterns.
An Integration of Augmented Reality in Structural Health Monitoring Workflow: Installation and Training
Structural Health Monitoring (SHM) has emerged as the most economical and efficient approach for maintaining large structures. A reliable SHM system assesses the structural condition by leveraging Industry 4.0 technologies. Advancements in smart sensor technology have made it feasible to monitor anomalies at hard-to-reach areas and along with a reliable wireless network, facilitated by the Internet of Things (IoT). This advancement is important as it eliminates the need for disassembly during inspections, thereby reducing time, effort, and costs. For a large-scale implementation of SHM however, one of the critical factors to consider is the installation process of the sensors itself. Say, in the case of installing hundreds of sensors on an aircraft panel or instrumenting an operating wind turbine blade or installing sensors on a busy bridge (traffic or rail), having a structured installation protocol with minimal loopholes is critical to ensure limited delays and non-conformities. Some of the challenges faced by sensor installers when instrumenting a large-scale (several sq.m area) include, a. comprehending complex structural drawings accurately and then marking the sensor positions on the structure. b. lack of standard working procedures. c. precision errors as a result of fatigue (repeated evaluation of sensor position from 2D drawings and manually correlating the same with a 3D structure) and adverse weather conditions (in the case of outdoor installation) that may add to any of the points mentioned previously. In order to overcome the challenges and to streamline the installation processes, we propose a novel workflow using Augmented Reality (AR) to guide and support installers, trainers and supervisors. The workflow is envisioned to guide installers with these complex installations and provide them the tools to visualize sensor positions, access installation guidebooks or relevant documents, report non-conformities and record pictures and videos post-installation, all hands-free. In addition to installation support, such a workflow aims to empower trainers and supervisors to access and support their installation team remotely in case of a need. We believe such a tool would enhance efficiency, reduce human errors and optimize the overall time needed for sensor installation.
Automated Quality Assessment of Precast Concrete Elements Using LiDAR and Augmented Reality
This paper presents an innovative system for enhancing quality assessment of precast concrete elements through the integration of LiDAR scanning technology and Augmented Reality (AR) visualization. Current precast concrete inspection practices often rely on random sampling due to time and cost constraints, which increases the risk of undetected defects. Our approach addresses this challenge by combining a mobile robotic platform (Cyclop) equipped with an RGBD camera for collecting 3D point cloud data, with an AR application that enables inspectors to visualize and interact with digital representations of the precast elements. The AR interface provides an intuitive experience where inspectors can manipulate holographic models through natural hand gestures, allowing them to examine complex geometries from multiple angles and identify dimensional deviations using visualizations. Laboratory testing conducted at the University of New Mexico's Vicon laboratory and field validation at Castillo Pre-Stressed in Belen, NM demonstrated the system's capabilities in accurately capturing dimensional data and generating interactive holograms for inspection. This technology offers significant advantages for precast concrete quality control by enabling more comprehensive inspections, reducing decision time, and providing permanent digital records of structural elements. The standardized procedure developed through this research provides clear guidelines for inspectors to effectively implement these advanced technologies in routine quality assessments.
Augmenting Human-Robot Collaboration for Real-Time Path Optimization
This paper describes a new framework for interaction between humans and robots, focusing on bidirectional data sharing between them in manufacturing environments. The research includes the integration of sensing systems through an Augmented Reality (AR) interface, which functions as a medium for sharing real-time information between humans and robots. One direction of information transmission involves humans communicating inputs such as tasks or interventions to the control systems of robotic mechanisms using hand-gesture and voice-command recognition offered by the AR interface. In the other direction, humans can perceive the robots’ intended motions and actions before execution in three-dimensional holograms created through light diffraction via the AR platform. The new bidirectional interaction integrates human inputs with robots’ control strategies and to enhance human perception of the robotic setting, where humans and robots can better coordinate their operations. Humans use AR to visualize robotic paths and understand their tasks for safety and oversight. They can make decisions by selecting, modifying, or approving robotic actions. Robots, equipped with kinematic, dynamic, and path-planning algorithms, generate path recommendations based on the task requirements, which humans can evaluate and use to make final decisions. The human perceives and evaluates paths calculated by the robot and can select those that meet specific priorities, such as safety or efficiency
Human-in-the-Loop Visualization and Emergency Remediation Using Augmented Reality and Sensors
The research summarized in this paper develops a new human-in-the-loop visualization and emergency remediation that enables emergency care providers to observe real-time measurements of the patience condition, with applications for health providers training at the school, emergency responders, and also potentially transforming the emergency responses. The main aspect of the proposed work focuses on the sharing of data for emergency responders to accelerate their understanding of the conduction around them. The new architecture transforms the observation and intervention of health care providers with access to sensor, data, and visualization without distraction from the patience, which visually informs the remediation in the event of emergencies. The new architecture enables for the first time new medical knowledge about the patient-symptom-data in a new bidirectional intervention that can accelerate identification of the emergency and a more accurate caring solution that can save lives in emergencies. The paper summarizes the design of a new application for trachea monitoring and chocking remediation, with specific attention to respiratory flow, as the data of respiration will be connected to a finite element model of the trachea of the patience, as well as better respiratory understanding with this interface.
Human-Wildfire Interfaces Towards Wildfire Mitigation in Wildfire and the Wildland Urban Interface (WUI) Communities
In recent years, wildfires have taken over our critical infrastructure, cities, and communities, and to date, efforts to mitigate wildfires are still unable to match the needs. More specifically, existing wildfire mitigation strategies in communities at risk do not prepare managers, authorities and emergency responders well to prevent or respond to such disasters. In this paper, we propose developing human-wildfire model interfaces that can assist responders, inspectors, and managers to better share and communicate wildfire risk and damage and change human behavior, from owners to emergency responders. The study of this interface will also explore the use of machine-aided digital twin experience aids pre-disasters that can assist emergency responders to make better decisions of evacuation or rescue to save lives. The goal of this research is to advance a new interface between the human, their environment during fire, and the perception of fire risk in the context of Wildfire and Wildland Urban Interface (WUI) using Augmented Reality (AR).
Comparative Evaluation of Neural Network Architectures for Generalizable Human Spatial Preference Prediction in Unseen Built Environments
The capacity to predict human spatial preferences within built environments is instrumental for developing Cyber-Physical-Social Infrastructure Systems (CPSIS). A significant challenge in this domain is the generalizability of preference models, particularly their efficacy in predicting preferences within environmental configurations not encountered during training. While deep learning models have shown promise in learning complex spatial and contextual dependencies, it remains unclear which neural network architectures are most effective at generalizing to unseen layouts. To address this, we conduct a comparative study of Graph Neural Networks, Convolutional Neural Networks, and standard feedforward Neural Networks using synthetic data generated from a simplified and synthetic pocket park environment. Beginning with this illustrative case study, allows for controlled analysis of each model’s ability to transfer learned preference patterns to unseen spatial scenarios. The models are evaluated based on their capacity to predict preferences influenced by heterogeneous physical, environmental, and social features. Generalizability score is calculated using the area under the precision-recall curve for the seen and unseen layouts. This generalizability score is appropriate for imbalanced data, providing insights into the suitability of each neural network architecture for preference-aware human behavior modeling in unseen built environments.
From Actions to Kinesics: Extracting Human Psychological States Through Bodily Movements
Understanding the dynamic relationship between humans and the built environment is a key challenge in disciplines ranging from environmental psychology to reinforcement learning (RL). A central obstacle in modeling these interactions is the inability to capture human psychological states in a way that is both generalizable and privacy preserving. Traditional methods rely on theoretical models or questionnaires, which are limited in scope, static, and labor intensive. We present a kinesics recognition framework that infers the communicative functions of human activity—known as kinesics—directly from 3D skeleton joint data. Combining a spatial-temporal graph convolutional network (ST-GCN) with a convolutional neural network (CNN), the framework leverages transfer learning to bypass the need for manually defined mappings between physical actions and psychological categories. The approach preserves user anonymity while uncovering latent structures in bodily movements that reflect cognitive and emotional states. Our results on the Dyadic User EngagemenT (DUET) dataset demonstrate that this method enables scalable, accurate, and human-centered modeling of behavior, offering a new pathway for enhancing RL-driven simulations of human-environment interaction.
Physics-Informed Machine Learning for Ultrasonic Guided Wave Full-Field Reconstruction and Damage Diagnosis
Ultrasonic guided waves have shown significant efficacy in structural health monitoring due to their increased sensitivity to structural changes in mechanical properties. Particularly, they are advantageous for the detection, localization, and quantification of damage in thin-walled structures. Traditional diagnostic methods that utilize ultrasonic guided waves often depend on signal pre-processing to extract pertinent features, which may inadvertently lead to the loss of crucial information. Recent advancements have seen the incorporation of neural networks to circumvent pre-processing and allow for the direct analysis of complex signals. However, the blackbox nature of neural networks, their dependence on extensive labeled data sets, and limitations in generalizing beyond their training domain present considerable challenges. This study introduces a novel unsupervised physics-informed machine learning methodology aimed at overcoming these obstacles. The damage diagnosis problem is framed as an inverse problem, specifically a full-waveform inversion task, with the objective of reconstructing the material distribution throughout the structure to identify potential discontinuities caused by damage. The methodology combines neural networks with a custom finite difference solver capable of efficiently simulating wave propagation, embedding the physics of the problem into the solution. The proposed methodology was validated against a case study of a metal plate affected by a single damage. Despite the computational challenges posed by the three-dimensional nature of the problem, the findings indicate that the approach effectively localizes and quantifies damage within the investigated structure.
Structural Health Monitoring of a Satellite Antenna: A Benchmark Between the Modal Method, iFEM Coupled with Modal Strain Expansion, and a Physics-Informed Neural Network
Shape sensing techniques for real-time deformation reconstruction are an increasingly important topic for Structural Health Monitoring (SHM), particularly in the con- text of digitalisation and digital twins. Artificial intelligence and artificial neural net- works are showing promising results for such tasks. Physics-Informed Neural Networks (PINN) have recently gained interest due to less training data-dependent predictions by incorporating physics laws into the training. To benchmark such a PINN for solving inverse problems in shape sensing, referred to as “iPINN”, this paper presents a study on its performance against the Modal Method (MM) as an established technique on the example of a composite space antenna. Moreover, a combination of the two mainstream methods for shape sensing is tested on the same space structure. Starting from a few strain measurements, the strain field is first expanded using a variation of the MM. Then, displacements are reconstructed using the inverse Finite Element Method (iFEM). This study shows that both the iPINN and the combination of MM and iFEM can produce predictions of deformed shapes, although further investigations are needed to improve their accuracy.
Parameter Estimation by Optimization of Interpretable Hyperparameters with Physics-Informed Gaussian Processes
In this work, the feasibility of a covariance-based parameter estimation approach utilising a physics-informed Gaussian process regression is considered and explored. There are a number of ways of incorporating physical assumptions or knowledge into a Gaussian process regression. One of the richest is through adapting the kernel to reflect some known behaviour of the system of interest. In some cases, it is possible to derive such covariance functions from stochastic differential equations, demonstrated for a singledegree- of-freedom (SDoF) oscillator in [1], where the result is a very flexible kernel that can be used in a variety of modelling situations. In this case, in aid of interpretability, the covariance function has hyperparameters that are the natural frequency and damping ratio of the SDoF system from which the covariance is derived. The current paper explores whether this model may feasibly be used for parameter estimation, and herein, an investigation into the SDoF kernel’s parameter estimation behaviour is conducted. This involves the visualisation of optimisation surfaces, exploration of what signal characteristics affect the estimates found, and finally, the proposal of a method to efficiently produce reliable parameter estimates. The estimates produced by this method show good agreement with the known system parameters over the range of systems tested, as well as good robustness to noise in the measured data.
Multifidelity Active Learning for the Fast Identification of Incipient Damages in Aerospace Structures
We propose MF-FREEDOM, a multifidelity active learning framework for efficient and accurate structural health monitoring of complex aerospace systems. The method combines (i) a two-stage informative signal compression that reduces data dimensional- ity while preserving critical features, and (ii) a multifidelity inference scheme that inte- grates high- and low-fidelity physics-based models to optimize diagnostic accuracy and computational efficiency. We apply MF-FREEDOM to detect incipient fractures in the carbon fiber skin of an aircraft wing achieving accurate damage detection performance with contained computational resources. By enhancing the efficiency and reliability of structural health monitoring, MF-FREEDOM supports the deployment of lightweight and maintainable structures playing a key role in advancing sustainable aviation.
Enhancing Bridge Structural Health Monitoring: Integrating Physics-Based Knowledge and Machine Learning for Temperature Compensation
This study explores the application of a grey-box modeling approach for bridge SHM, integrating physics-based principles with machine learning (ML) techniques to enhance predictive accuracy. Using data collected from a tied-arch bridge, including acceleration, and temperature measurements, the study first evaluates different ML models, such as Linear Regression and Gradient Boosting Regressor, to eliminate the temperature effect from the natural frequency of a bridge cable. While purely data- driven models yield reasonable predictions, their accuracy improves when enhanced with prior physics knowledge. Building upon this, a grey-box model is applied to further refine the temperaturecompensation process, assessing its effectiveness in the SHM domain. The findings demonstrate that a grey-box model offers a more robust and reliable solution by leveraging fundamental physical principles alongside data-driven learning. This approach proves particularly beneficial in real-world SHM scenarios, where environmental and operational variability complicate damage detection and monitoring.
Damage Detection and Quantification Through Differentiable Numerical Solvers
Structural health monitoring strategies typically attempt to identify and quantify damage by utilizing sensor data. This is typically referred to as the inverse problem. While sensor data of the response of dynamical systems is usually abundant, data of damage location and quantity are typically difficult to acquire, even in lab settings. This lack of labels hinders traditional supervised learning, as the inverse problem is primarily concerned with changes in certain parameters of the dynamical system (e.g., stiffness degradation). This issue necessitates some form of parametric estimation, where the underlying structure of the physics model, parameterized by “damage-sensitive” parameters, is already known. This inverse problem setup naturally lends itself to Partial Differential Equation (PDE)-constrained optimization, where the objective function is the misfit between sensor data and the predicted sensor data of the physics model, and is constrained by a PDE residual equality constraint. Several methods exist to solve this constrained optimization problem, some using non-gradient-based optimization, e.g., Bayesian optimization. In this work, we use a gradient-based optimization method, namely automatic differentiation, to determine the gradients necessary for optimization. This is possible due to the ability to represent numerical solvers as computational graphs, which can be differentiated. This method, a variant of the primal method, offers the additional benefit of incorporating physics as a hard constraint, unlike other methods, such as the penalty method. Case studies on synthetic problems related to wind turbines are presented and evaluated, and practical workarounds for memory constraints are discussed.
Physics-Informed Machine Learning for Structural Health Monitoring of Aerospace Composite Structures
This paper presents advanced structural health monitoring (SHM) methods for aerospace composite structures, which pose unique challenges due to sensor placement, cost, and environmental exposure. We introduce novel, physics-disciplined, data-driven approaches developed through two European Union projects. The first technique embeds glass-coated copper microwires in carbon-fibre composites, exploiting their Giant Magnetoimpedance (GMI) response to detect, classify, and quantify damage under stress. The second approach supports damage monitoring in composite liquid hydrogen tanks using Fibre Bragg Grating (FBG) sensors, addressing the challenges of conformal geometry and strict leakage tolerance where conventional diagnostics are inadequate. Physics-informed machine learning algorithms are developed for both systems. Finite element simulations inform neural network architecture and feature selection, while simulated signals guide strain and damage modelling. Experimental and simulation-based validation confirms high accuracy in damage detection and characterisation. This work was funded by the European Union under the Horizon Europe grant 101056884 and by the EU Clean Hydrogen Partnership under Grant Agreement 101101404. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or Clean Hydrogen Joint Undertaking. Neither the European Union nor the granting authority can be held responsible for them.
iFEM Implementation on a Rotating Shaft for Imbalance Identification
Structural Health Monitoring (SHM) is crucial for ensuring the safety and reliability of rotating machinery. In rotating shafts, structural damage can affect force distribution and alter dynamic behavior. This study conducts a preliminary numerical investigation into the application of the inverse Finite Element Method (iFEM) for rotor monitoring. Based on Euler-Bernoulli beam theory, the iFEM algorithm reconstructs the full three-dimensional deformation field through beam elements of inverse fi nite ñth-order formulation. Static and dynamic Finite Element Method (FEM) simulations assess the accuracy of the methodology under varying loading and boundary conditions. To simulate real-world sensor inaccuracies, Gaussian noise is introduced into the strain data, replicating measurement uncertainties typical of Fiber Bragg Grating (FBG) optical sensors. Results demonstrate the strong iFEM capability to capture deformation patterns, paving the way for iFEM real-time monitoring of a rotating shaft. Its sensitivity to structural changes highlights its suitability for predictive maintenance and fault diagnostics in rotating machinery. This study lays the groundwork for further research into iFEMbased SHM implementations, supporting research advancements and new industrial applications.
Physics-Informed Filtering for Ultrasonic Guided Wave Structural Health Monitoring
This paper presents a methodology for physics-informed filtering, applied to ultrasonic guided waves. The physics-informed filters are derived by integrating physicsbased constraints into an optimization framework, where the cost function comprises of two components: a reconstruction error term and a regularization term. The reconstruction error term ensures the filtered data closely aligns with the original measurements while the regularization term enforces physical laws represented by a linear operator. We derive a general closed-form solution for these filters and then apply them with the two-dimensional Helmholtz equation. We apply these filters, using various parameters, to ultrasonic guided wave data collected by a laser Doppler vibrometer. We demonstrate the ability to characterize damage regions with thickness losses from the filtered data.
Physics-Informed Machine Learning-Driven Structural Digital Twin for Damage Identification
With advancements in AI, data-driven methods such as autoencoders (AEs) have been widely used for damage identification through anomaly detection. However, AEbased methods primarily learn identity mappings from healthy-state data, making them less effective in detecting subtle damage. This study presents a physics-informed, machine learning-driven structural digital twin (SDT) framework for damage identification using anomaly detection. By incorporating physics-informed neural networks (PINNs), the framework reduces discrepancies between finite element model (FEM) predictions and real structural responses, enabling more accurate anomaly detection. The proposed approach is evaluated using the numerical ASCE benchmark structure. It outperforms AE and LSTM-AE baseline methods in comparatively smaller damage scenarios.
Structural Damage Identification Using Physics-Informed Neural Network and Transfer Learning
In structural health monitoring (SHM), finite element models (FEMs) have been used to simulate diverse damage scenarios for training deep learning-based structural damage identification models; however, discrepancies between FEMs and actual structures persist. To bridge this gap, we propose a hierarchical physics-informed domain adaptation (HierPhyDA) approach that synthesizes features from FEMs to mirror those from actual structures and emulates authentic vibration signatures. The proposed solution employs an initial phase of unsupervised anomaly detection using a deep autoencoder approach, followed by a novel physics-informed domain adaptation method that serves as a digital twin of the physical structure. This approach is rigorously evaluated through numerical studies using the ASCE benchmark structure. The results show that the proposed approach outperforms state-of-the-art methods when damage cases from the actual structure are excluded from training and are mutually exclusive from those generated by the FEM.
Structural Analysis and Damage Detection in Composite Materials Applications
The increasing use of composite materials in aerospace, automotive, and civil engineering demands robust approaches for structural health monitoring (SHM) and failure prediction. Due to their high strength-to-weight ratio and corrosion resistance, fiberreinforced polymers (FRPs) are essential in modern lightweight structures such as unmanned aerial vehicles (UAVs) [1]. However, they remain prone to fiber breakage, matrix cracking, and interlaminar delamination, which are often difficult to detect visually, making real-time monitoring critical for safety. This study presents a structural analysis of a carbon-fiber UAV wing using finite element analysis (FEA) in ANSYS to evaluate stress distributions, deformations, and failure modes under various loading scenarios, including progressive damage and fatigue. The Hashin and Tsai–Wu criteria were implemented to differentiate fiber- and matrix-dominated failures, including interlaminar fracture. Numerical results were compared with experimental data from the literature [5] to validate the approach. These results support the integration of FEA with SHM systems using Fiber Bragg Grating (FBG) sensors and Digital Twins for lightweight, sustainable composite structures in aerospace and civil infrastructure.
A Multifunctional Smart Metal Beam with Sub-Surface Embedded Sensors for Real-Time Structural Health Monitoring
Smart Metallic Structures (SMS) with embedded sensors have gained significant at- tention for Structural Health Monitoring (SHM) applications. This study develops a “Smart Beam” with sub-surface embedded sensors, fabricated through a hybrid manu- facturing approach that integrates additive and subtractive techniques. The sensor net- work comprises commercial off-the-shelf (COTS) sensors, including strain gauges and resistance temperature detectors (RTDs), embedded into an additively manufactured aluminum beam (Al 5356) for real-time structural health and environmental monitoring. To evaluate its performance and reliability, the fabricated beam is subjected to a series of SHM assessments, including steady-state heating, transient heating, and three-point bending experiments. The results from these tests confirm the Smart Beam’s structural integrity and sensor functionality, with quantitative assessments indicating high-fidelity temperature measurements (≤ 0.5% error) and consistent strain response under both the linear and failure regions. Despite a moderate mechanical debit—20.6% reduction in flexural modulus and 18.9% in yield strength—the Smart Beam effectively captured both mechanical and thermal responses throughout testing, confirming its functional integrity and sensing reliability. The developed Smart Beam, along with its hybrid manufacturing approach, holds significant potential for advanced SHM applications.
Digital Twin Deep Link: Physics Based Model with Scientific Machine Learning for Simultaneous Crack and Wave Propagation
The integration of Digital Twin technology with Physics based models and Scientific Machine Learning (SciML) offers a transformative approach for Nondestructive Evolution (NDE) and Structural Health Monitoring (SHM). Detection and prediction of crack evolution under local stress through wave propagation demands physics-based understanding of crack-wave interaction. Traditional computational methods struggle to simultaneously capture crack growth dynamics and guided wave interactions due to the complexities of remeshing and high computational costs. The study demonstrates the simultaneous simulation of crack propagation and guided wave interactions without requiring mesh updates. Further, by leveraging physics-informed neural networks (PINNs) under SciML approaches, a Digital Twin framework can bridge this gap, providing real-time, data-driven insights into Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE). This study presents a Digital Twin Deep Link framework that coupled physics-based models with SciML to accurately predict crack initiation, growth patterns, and guided wave interactions in stiffened structures. Case studies illustrate how physics-informed AI enables the identification of crack signatures in sensor data, providing a robust mechanism for defect detection and material state assessment. The results highlight the potential of SciML-powered Digital Twins in SHM and NDE, paving the way for AI-driven diagnostics and autonomous monitoring systems.
On Wave Field Kinematics Estimation from Structural Vibration Measurements Via Physics Informed Latent Force Models
This study presents the application of the recently introduced Physics Informed Latent Force Models (PILFM) for estimating structural responses and wave kinematics (i.e., water particle velocity and acceleration) in the vicinity of an offshore structure, where a limited number of vibration responses are measured. This can enhance the accuracy of estimated structural responses, such as local stresses, that can be used for assessing the remaining fatigue life of the structure. PILFM integrates a model of the structural system that describes the structural vibration of the structure under wave loading, with known wave theories that describe the spatio-temporal behavior of the waves. A data-driven state space model is obtained based on the wave theories, and a Kalman Filter and Smoother are employed for the efficient estimation of the structural response and the wave kinematics. The current study presents a proof-of-concept of the proposed methodology, illustrated via a 2D structure subjected to stochastic waves. A few structural response measurements enable the estimation of the field kinematics in the vicinity of the structure and the full structural response.
Deterioration of Bridge Deck Element Based on Neutral Axis Analysis
This study investigates the use of the neutral axis as a damage-sensitive feature for structural health monitoring in composite bridge sections. A methodology was developed to calculate the position of the neutral axis and evaluate its changes over time, involving strain measurements from sensors installed along the cross-section of a bridge specimen tested at The BEAST® (Bridge Evaluation and Accelerated Structural Testing lab) at Rutgers, The State University of New Jersey. The BEAST® laboratory is the first full-scale facility designed for accelerated bridge testing under controlled conditions. It applies repeated vehicle loading and environmental cycles, such as freeze-thaw and de-icing, to simulate decades of deterioration in just a few months. This unique setup provides continuous monitoring data to support the evaluation of structural performance over time. The results reported herein demonstrate that increasing neutral axis-centroid deviations correlate with bridge deck degradation. The findings validate the feasibility of neutral axis-based damage detection, highlighting its applicability in structural health monitoring applications.
Preliminary Study on Application of Population-Based Structural Health Monitoring for Ferry Quays in Norway
Ferry quays are critical components of transportation infrastructure, serving as vital nodes that support the movement of people, goods, and services. This is especially important in coastal regions such as western Norway, where ferry quays provide essential connectivity across wide and deep fjords that are otherwise impassable. Despite their strategic importance, research into the structural performance and health monitoring of ferry quay infrastructure remains limited. This study explores the potential for applying a Population-Based Structural Health Monitoring (PBSHM) approach to ferry quays in Norway. In contrast to conventional SHM techniques, which typically focus on monitoring individual structures, PBSHM utilizes data from a broader population of similar structures to identify shared patterns, detect anomalies, and improve condition assessment across the network. A fundamental requirement for implementing PBSHM is the ability to evaluate and quantify the structural similarity between assets in the population — a key focus of this research. Given the number, accessibility, and structural similarity of ferry quays across Norway, this study suggests that PBSHM holds significant promise as a scalable and effective strategy for enhancing the monitoring and maintenance of this critical infrastructure.
Improving Off-System Bridge Monitoring: An AI-Driven Methodology for Enhanced Condition Prediction
Off-system bridges, especially in rural and underserved areas, play a vital role in connecting communities to essential services but often suffer from neglect and underinvestment. These structures are frequently excluded from comprehensive inspection programs, resulting in limited data and a high risk of undetected deterioration. This study addresses the critical challenge of class imbalance in bridge condition datasets by introducing a machine learning framework that integrates Boruta feature selection, Ridge regularization, TomekLinks sampling, and a Generative Adversarial Network trained with focal loss (GAN-FL). The model aims to enhance the accuracy and interpretability of bridge deck condition predictions. Despite improvements in performance for majority classes, minority class predictions remained a challenge, with certain conditions (e.g., 4, 5, and 8) showing low F1-scores. However, the use of GAN-FL led to measurable gains in balancing recall and precision across classes. The proposed approach demonstrates the potential to support risk-informed maintenance decisions, prioritize inspections, and improve safety outcomes for off-system bridges. Future work will focus on improving performance for underrepresented classes and increasing model transparency to support adoption by transportation agencies.
Effect of Temperature on Bridge Health Monitoring Using Bayesian Bridge Weigh-in-Motion
The importance of bridge health monitoring (BHM) has grown significantly due to its critical role in ensuring bridge safety and longevity with increasing traffic loads and aging infrastructure. Bridge weigh-in-motion (B-WIM) technology has emerged as a promising alternative to traditional methods for assessing the structural health of bridges. The bridge influence line (BIL), backbone of B-WIM system, contains substantial structural information, and any change observed in BIL can be used for BHM. Despite advancements, transitioning BHM from research to practice is hindered by environmental and operational conditions (EOCs) variability, which impacts BIL estimation accuracy and, thus, BHM effectiveness. Simultaneously, temperature effects stand out as a paramount concern for long-term BHM and are anticipated to introduce additional environmental variability, particularly through temperature fluctuations. Recent studies suggest that bridge deformation due to temperature-induced expansions and contractions can be equal to or greater than that induced by vehicular load. This can potentially affect the BIL estimation and mask the effect of damage, leading to false damage alarms. Several studies have explored various BHM approaches using B-WIM system in the recent decade. However, the specific impact of temperature on B-WIM-based BHM remains understudied. This highlights the need to evaluate the performance of B-WIM-based BHM methods under the influence of temperature variations. To address this, the study aims to evaluate BHM performance under temperature variations using temperature data of Patna city collected over the last two decades. This approach recognizes that substantial temperature variations can significantly affect bridge responses. Additionally, the study incorporates advancements in B-WIM systems, particularly using the evolving Bayesian framework, which is promising in improving B-WIM algorithms. Bayesian B-WIM (BB-WIM) incorporates prior knowledge with real-time observations to update probability distributions, enhancing accuracy and enabling uncertainty quantification. The methodology employs a 3D finite element (FE) model of a real bridge, incorporating various temperature conditions and damage severities. The FE model is validated by matching modal frequencies up to the fourth mode with those of the real bridge within acceptable limits. Different bridge response time histories (RTHs) caused by vehicular movements over the bridge are obtained at every quarter location under a longitudinal beam below the deck. The obtained bridge RTHs, such as strain, acceleration, and deflection, are then fed into the BB-WIM algorithm to estimate the BIL. Finally, gross vehicle weight (GVW) -based damage indicator values are calculated for performance assessment under temperature variation. The findings from this study are then summarized with a focus on highlighting the future aspects and practical implications for improving BHM systems.
Examining Environmental Changes on Concrete Bridges with the Help of Local Distance Correlation
To investigate the influence of environmental effects on concrete bridges, local distance correlation (LDC) is used due to its ability to accommodate the nature of environmental data, namely local stationarity. Similarities and differences of these influential variables can be identified with the help of LDC. Moreover, LDC pattern based upon weather phenomena and natural frequencies as well as crack widths show relations between their respective temporal progresses. These findings are illustrated by data from a test bridge in Munich and a bridge for vehicles in Bochum, Germany.
Multi-Task Damage Identification Method for Composite Stiffened Plate Based on Lamb Wave and Multi-Task Deep Learning
With the rapid growth of the demand for structural health monitoring in the aerospace field, especially under complex and changeable structural and damage cases, damage detection methods are facing the challenges of insufficient detection accuracy. To solve this problem, this paper proposes a detection method of damage location of composite stiffened plates based on Lamb wave and multi-task deep learning. In this paper, experiments are carried out on a composite stiffened plate with two stiffeners. Firstly, the damage monitoring area is divided into four sub-areas, each sub-area is further subdivided into nine grids, and five damage signals with different damage sizes are collected at the center of each grid. Through the differential operation between the baseline signal and the damaged signal, the damage scattering signal is extracted as the input of the deep learning model. The model training is only based on the data of the first sub-area, and its generalization ability is evaluated through two test sets: one test set contains the random signals of the non-center location of the first sub-area, and the other test set comes from the random signals of the other three sub-areas. Then, the Inception module and BiLSTM is used to extract the multi-level and multi-scale features of the damage signal, which effectively captures the features of the signal, thus significantly improving the accuracy of damage location. Finally, two output branches are designed to realize the multi-task learning target model, which can predict the x and y coordinates of the damage location at the same time. The experimental results show that this method can accurately identify the damage at any location in the monitoring area. In the tests of different sub-areas, the model still shows good robustness and generalization ability, showing a certain application potential.
Digital Twin System for Structural Damage Monitoring Based on Lamb Wave Feature Enhancement with Imbalanced Few-Shot Learning and Interactive Transfer
As structural damage patterns and service environments become more complex, digital twin-based structural health monitoring (SHM) with its unique advantages can compensate for the limitations of data-driven methods regarding data dependency and model interpretability. However, it still faces challenges in modeling complexity, simulation accuracy, and discrepancies between real and virtual features. This study proposes a balanced fidelity digital twin for structural damage monitoring based on Lamb wave multilevel feature enhancement and adaptive space interaction. Firstly, multilevel refined features are extracted from few-shot guided wave signals obtained in physical and digital space, and the adversarial synthetic balancing algorithm (ASBA) is proposed for feature enhancement. Additionally, the learning phase of the damage monitoring model based on the feature mapping convolutional network (F-MCN) is driven by virtual samples of readily accessible balanced fidelity in digital space. To reduce the feature distributional difference between the two spaces, an interactive transfer approach is introduced to establish a shared feature digital twin space. Overall, this study provides a feasible technique to enhance the accessibility and generalizability of digital twins for real engineering structures.
High Resolution Ultrasonic Imaging in Stiffened Composite Panels by Robust Data-Driven Matched Field Processing
Ultrasonic imaging techniques are commonly deployed using sparse transducer arrays operated under plane wave beamforming such as delay-and-sum or time migration methods. However, in complex aerospace structures such as stiffened composite panels, wave refraction and dispersion effects significantly complicate the beamforming process. To address these challenges, the complexity of the propagating waves for impact and damage imaging can be best leveraged by using data-driven Matched Field Processing (MFP) techniques. These methods are effectively used in underwater acoustics but much less employed in SHM/NDE. This work introduces both passive and active beamforming techniques within the data-driven MFP framework using a sparse array of ultrasonic piezoelectric transducers applied to a complex aerospace panel. In particular, the broadband Coherent White Noise Constraint (C- WNC) beamformer is applied to cross correlate data-driven models and the data of interest. The C-WNC algorithm is robust against model mismatch and achieves high resolution and contrast. Its efficacy is demonstrated through passive imaging of hammer impacts on a stiffened composite panel. For damage imaging, where the damage scatterings typically act as secondary sources, conventional adaptive beamformers primarily focus on the active sources, such as a transmitter element or hammer impacts. To increase the sensitivity to structural damage in the covariance matrix, the “active” inter-element transfer functions are reconstructed by estimating the normalized cross- power spectrum between two receiver elements. This reconstruction is achieved passively by observing the structural response to a series of hammer impacts. By averaging the covariance matrix using only few hammer impacts, the C-WNC algorithm is shown to successfully image simulated damage in the stiffened panel while suppressing the “louder” impacts.
Damage Classification in Composite Materials for UAVs Using FBG Sensors and Artificial Neural Networks
The structural integrity of composite materials in unmanned aerial vehicles (UAVs) is critical for ensuring flight performance, safety, and durability under various opera- tional conditions. These materials are subject to progressive damage accumulation due to external loads, fatigue cycles, and environmental factors, which can compromise their mechanical properties over time. This work proposes a damage classification system based on Fiber Bragg Grating (FBG) sensors and Artificial Neural Networks (ANN) to detect and classify structural damage in composite material parts of UAVs. The valida- tion of the methodology is performed in data from a composite wing section represen- tative of an unmanned aerial vehicle (UAV) structure. The system integrates thirty-two FBG sensors strategically placed to measure strain variations through wavelength shifts, capturing subtle deformations indicative of material degradation. The sensor data collected are processed to classify the material conditions into five damage states ranging from undamaged to increasingly deteriorated structural responses. The neural network model, designed as a multilayer perceptron (MLP), receives wave- length shifts as input, leveraging supervised learning to achieve accurate damage classification. The model is trained on a diverse dataset generated from controlled experiments to enhance robustness, where composite UAV materials are subjected to various stress conditions to ensure that the ANN can generalize damage classification across different failure modes commonly observed in UAV structures. 210 labeled experiments were used for training and validation, providing the model with a broad representation of structural responses. The results demonstrate that the proposed ANN-based approach achieves high classification accuracy, effectively distinguishing different levels of mate- rial degradation based on the FBG sensor data.
Optimization and Performance Analysis of an FPGA-Based Structural Health Monitoring System for Aerospace Composite Structures
Structural Health Monitoring (SHM) is a transformative technology in aerospace engineering, enabling real-time assessment of aircraft structural integrity to enhance safety, reliability, and maintenance efficiency. Traditional inspection methods, such as visual inspections and non-destructive testing (NDT), require manual labor and aircraft downtime. In contrast, a SHM system offers continuous and automated monitoring, thereby reducing operational costs and improving aircraft availability. This paper presents an optimization study of a previously developed FPGA-based SHM system for carbon fiber-reinforced polymer (CFRP) laminates. The system uses piezoelectric transducers (PZTs) to both excite and receive Lamb waves and employs signal processing and machine learning techniques for damage detection. In the original implementation, signal processing steps such as discrete wavelet transform (DWT) and feature extraction were executed in MATLAB. In this work, a fixed-point hardware implementation of the DWT was developed and integrated into the acquisition pipeline. The DWT stage, originally implemented in MATLAB, was migrated to hardware, resulting in a processing time reduction from 1.43 seconds to 5.68 microseconds, which led to over 250,000 times speedup. The classification stage, based on a support vector machine (SVM) enhanced with Mahalanobis distance, was preserved in hardware. A comparative analysis shows that the embedded version achieves the same classification accuracy as the softwarebased approach, with no loss of precision. The results demonstrate that feature extraction can be executed in real-time, advancing the system toward fully embedded SHM applications.
Towards an Efficient Digital Twin Framework for Fiber Composite Structures Using Optimal Sensor Placement Techniques
Structural health monitoring (SHM) is a critical component in the safe and efficient operation of aerospace structures. Preliminary studies were carried out to investigate the suitability of sparse sensor measurements to estimate the response of fiber composite structures and predict their performance. The displacement response was approximated using a linear combination of a set of basis vectors, where the basis vectors were extracted from a snapshot matrix of the structure’s response under various loads. The coefficients of the basis vectors were assessed in real time using the sensor measurements at sparse locations chosen based on the pivot locations of the QR algorithm. This displacement map is then leveraged to compute critical SHM parameters such as the buckling load. The proposed method was applied to a fiber composite plate subjected to in-plane buckling loads. It was shown that the displacement vectors were mapped with L2-norm errors of mean 0.003% using measurements at just six locations, leading to stress distribution and buckling load estimations with very small errors.
Recent Advances in Nonlinear Ultrasonic Guided Wave Techniques for NDT & SHM
The use of nonlinear ultrasonic guided waves for nondestructive testing and structural health monitoring has drawn increasing concern. Higher harmonic generation (HHG) of ultrasonic guided wave propagation was firstly investigated and applied for damage assessment. The key concept for the HHG-based method is to find the “phase matching” modes to generate accumulate nonlinear components versus propagation distance, to improve the signal-to-noise ratio. The major difficulty with HHG method as the NDT technique lies in difficult to distinguish the material nonlinearity from the instrumental systems. In addition, HHG based nonlinear technique can only be used to evaluate the total nonlinearity over the region between transmitter and receiver. Subsequently, guided wave mixing (GWM) technique was proposed. GWM technique has some unique advantages, such as frequency selectivity, which can intentionally avoid receiving unexpected harmonic components induced by instrumental systems, as well as the feature of spatial selectivity of scanning wave mixing zone, which can be used to identify the local defect. Recently, static component generation (SCG) or quasi- static component (QSC) generation of guided wave pulse signals, was systematically explored and proposed. It is shown that the QSC based guided wave technique is an effective means to assess large structures with low attenuative effect. In addition, another breakthrough of nonlinear guided wave, is the expansive investigation on the acoustic nonlinear response of guided wave propagation in the topographic feature structures. The key concept is to build analytical model to find the modes with acoustic energy trapping effect in the feature region. The development of nonlinear feature guided waves (FGWs) technique provide a promising alternative for inspection of topographic feature region of complex structures.
In Situ Mapping of State of Charge of Lithium-ion Battery by Quasi-Static Components of Guided Wave
Accurately probing the interaction mechanism between acoustic signals and the state of charge (SOC) in lithium-ion batteries (LIBs) is challenging. This study introduces a novel method based on the quasi-static component of nonlinear guided waves (QSC-NGW) to reveal the evolution of electrode materials during LIB charging and discharging. Utilizing an empirical formula and phase-reversal technique, the method successfully captures high signal-to-noise ratio (SNR) QSC-NGW signals within LIBs. This approach effectively establishes a mapping relationship between the time-frequency domain eigenparameters of the signal and the battery's SOC. Subsequent in-situ characterization techniques, including scanning electron microscopy (SEM), enabled the observation of lithium-ion deintercalation processes within the electrodes. This facilitated precise identification of electrode surface structural evolution and direct extraction of mechanical property variations. These findings corroborate the efficacy of the QSC-NGW method. Compared to conventional linear acoustic techniques for extracting battery state parameters, the QSC-NGW method exhibits superior directivity and strong interpretability. Crucially, it eliminates the need for prior prediction of internal battery material parameters or the construction of complex guided wave dispersion models. The integration of the QSC-NGW method with in-situ characterization effectively captures SOC changes during cycling, demonstrating its broad application potential.
Slow Dynamics for Concrete Strength Evaluation
This study explores the use of slow dynamics behavior to estimate the compressive strength of concrete. Slow dynamics, a nonlinear elastic phenomenon observed in heterogeneous materials like concrete, involves stiffness reduction under conditioning followed by a logarithmic recovery over time. Concrete specimens with compressive strengths of 32, 40, 49, and 56 MPa were tested. Each was subjected to a 6 kN load while ultrasonic signals were monitored to track relative velocity changes (dv/v) during recovery. Higher strength specimens showed larger velocity drops and faster recovery. The 56 MPa samples had dv/v drops that were 2.23, 1.80, and 1.37 times greater, and recovery rates that were 2.09, 1.84, and 1.37 times faster, than those of the 32, 40, and 49 MPa groups. In contrast, ASR damaged specimens exhibited greater sensitivity to conditioning but recovery rates that did not scale with the velocity drop, unlike the proportional behavior observed in intact samples.
Detection and Localization of Micro-Cracks in Plate Structures Using Topological Acoustic Sensing
This work presents an emerging technique called topological acoustic (TA) sensing for identifying micro-cracks in plate structures. The geometric phase change – index (GPC-I) from TA sensing is defined as the damage index (DI) to indicate the presence of micro-cracks. Then a localization methodology adopting GPC-I is proposed to identify random locations of micro-cracks occurrence in plate structures. Finite element method (FEM) based analysis is carried out in Abaqus/CAE software for 1) modeling contact acoustic nonlinearity (CAN) arising from micro-cracks and 2) verifying the proposed localization method. Numerical results show that micro-cracks induced CAN can be properly generated in FEM with artificially changing the cracks’ surface properties. Random locations of micro-cracks occurrence in plate structures can be identified accurately with the proposed localization method. Compared to the amplitude ratio (AR) parameter, the newly developed GPC-I can give more robust and distinguishing features for localizing micro-cracks. The velocity ratio (VR) parameter fails to capture nonlinear response arising from micro-cracks. This work is the first attempt to investigate the capability of GPC-I in capturing nonlinear response generated from micro-cracks. Such capability of GPC-I for topological acoustic sensing can provide new insight to benefit nonlinear ultrasonic community.
Damage Identification of Composite Laminate Impact Delaminations with Dual-Tone Steady-State Ultrasonic Excitation and Nonlinear Signature Analysis
Composite layered materials offer many engineering advantages including high strength-to-weight ratio, tailorable material properties, and high fatigue resistance. Detection of small intra-layer delaminations that occur during manufacturing or while in the service environment are critical to ensure that the structure maintains its designed performance. Nondestructive evaluation (NDE) methods, such as ultrasonic testing, can identify hidden defects and damage, but detection can be challenging because the separated layers often remain in physical contact and prevent ultrasonic waves from scattering. Simultaneous multi-frequency excitation has the ability to incite contact acoustic nonlinearities and unique ultrasonic scattering patterns which can be observed within the ultrasonic signatures of the harmonics and mixing frequencies. Acoustic steady-state excitation spatial spectroscopy (ASSESS) is a full-field ultrasonic NDE inspection technique. ASSESS implements a steady-state ultrasonic excitation using a transducer and rapidly measures a structure’s surface velocity response using a laser Doppler vibrometer (LDV). The measurement product is a complex-valued wavefield response mapped to the surface geometry. This research investigates the presence of nonlinear signatures in a dual tone ASSESS measurement of a composite laminate panel with three impact delaminations. From a single measurement, complex velocity wavefield maps are calculated at the two excitation frequencies and additional harmonic and mixing frequencies. Contact acoustic nonlinearity signatures are identified as changes in response amplitude over certain frequencies. Spatial and temporal frequency and image processing methods are applied to individual wavefield images to identify damage locations.
A Model-Assisted Probability of Detection (MAPOD) Approach for Enhancing the Reliability of Ultrasonic Inspection in Adhesive Bonded Joints
This study presents a model-assisted technique to investigate the probability of detection (POD) for ultrasonic inspection of adhesive joints with defects (brass inclusions and delamination). Numerical simulations using CIVA software were conducted to predict how ultrasonic waves respond to flaws of varying sizes from 0.01 mm to 5 mm. A sensitivity analysis was performed to simplify the physics-based model by identifying the most influential parameter using a meta-model. The model was further improved and calibrated to generate a POD curve, enabling accurate determination of the smallest detectable crack size. Moreover, this study focused on the implementation of a customdeveloped Python script to integrate ultrasonic features (absolute time-of-flight difference, maximum amplitude, and absolute energy) for evaluating the POD curve, while also addressing statistical uncertainties arising from measurement noise, material variability, and discrepancies in simulation models. Confidence intervals were added to reflect uncertainties in the simulation parameters and to predict potential differences between both simulation and real adhesive-bonded structural integrity. The results of this study revealed that the potential of model-assisted probability of detection (MAPOD) analysis enhanced the detection and measurement of defects in adhesivebonded joints. Overall, these results make a valuable contribution to improving quality control, effective structural health monitoring and advancement in reliable predictive methods for non-destructive testing (NDT).
Noise Suppression of SAFT Ultrasound Video Using Eigen Filters
In ultrasound scanning videos, noise and sub-stationary artifacts can severely impair the detection of the intended targets. This paper advances an unsupervised learning framework for noise suppression in ultrasound videos based on eigen filter concept. Specifically, the study focuses on the removal of artifacts in Synthetic Aperture Focusing Technique (SAFT) beamformed videos created by a moving transducer array in a scanning mode. The specific application is in-motion imaging of internal flaws in railroad tracks using a transducer array mounted in a Rolling Search Unit (RSU). By employing Singular Value Decomposition (SVD) analysis, an ultrasound video is decomposed into principal components through their time-space coherence/correlation. Artifacts can be eliminated by projecting out the associated principal components from the video (i.e. eigen filtering). A novel recursive algorithm with signal rectification is proposed to allow SVD filters to better capture the subtle movement of artifacts in ultrasound tomography. As opposed to the prior knowledge of a stopband in conventional SVD filters, the recursive SVD filter only removes the primary eigen mode until convergence. The proposed approach is validated through experiments using an RSU hosting a 25-element linear transducer array in defected rail sections in the laboratory and in the field. The results demonstrate a remarkable filtering ability, where artifacts (e.g. reflections at the wheel-rail interface, reverberations within the wheel, reflections from the bottom flange of the rail head) are successfully eliminated and only the internal flaws are imaged with high accuracy. This approach is applicable to any ultrasound imaging test where “loud” artifacts are present in addition to the intended targets.
Quantifying the Required Sample Size for Desired Confidence in Damage Detection of Structural Health Monitoring
In the Structural Health Monitoring (SHM) context, the most critical question is often: Is the system damaged or not? Methods to detect damage rely on identifying novelties in the system response, where significant deviations from training data indicate structural damage. However, this process depends on a threshold, typically defined as a single crisp value based on data distribution or representativeness assumptions. If natural variability, i.e. aleatory uncertainty, is not fully captured, false positives may occur. Probabilistic approaches address aleatory uncertainty, while epistemic uncertainty – arising from lack of knowledge and imprecision – is often modelled using set theory, including interval, fuzzy sets, and other imprecise probability frameworks. Recent research suggests that integrating both types of uncertainty enhances confidence in structural integrity assessments, and fuzzy probabilities offer one feasible approach. This study develops a methodology using fuzzy probabilities to determine the required sample size for a predefined confidence level in damage detection. The fuzzy membership function aggregates plausible intervals into a unified framework, including quantifying how many samples are needed to achieve a certain confidence level. A two-degreeof- freedom system validates the approach, demonstrating its effectiveness in detecting damage compared to a crisp value-based threshold.
Minimum Detectable Damage on Magerholm Linkspan Via Finite Element Model Updating and Damage Simulation
Norway’s extensive coastline and island communities rely heavily on linkspans, critical infrastructure vital for economic development. Subjected to harsh environmental conditions and demanding operational schedules, they are susceptible to accelerated degradation, highlighting the need for robust Structural Health Monitoring (SHM). This study applies and expands upon a recently proposed Minimum Detectable Damage (MDD) framework to establish curves showing variation of probability of damage detection with respect to different damage levels. The investigation focuses on the lifting towers that vertically support the linkspan at the Magerholm ferry quay, using an updated Finite Element (FE) model of the structure. Temperature variability and measurement noise are considered to reflect real-world monitoring conditions. This research employs and compares four different outlier detection methodologies to derive Damage Indices (DIs) from simulated natural frequencies.
Statistical Models for Temperature Prediction in Civil Structures
This paper presents two models for reconstructing the thermal field of civil structures without relying on thermocouple data, using instead information from weather stations located near the structure under analysis. The first model estimates temperature through a linear correlation with air temperature, while the second adopts a physics-based approach that also accounts for the effects of solar radiation. Both models were applied to the bell tower of Portogruaro. The physics-based model demonstrated high accuracy, with a prediction uncertainty of 1.50 °C and a correlation coefficient of 0.99 between predicted and measured temperatures.
Eccentric Virtual Array Source Aperture (EVASA)—Ultrasound Imaging Technique Using Phased Array Excitation
The traditional phased array ultrasound technique (PAUT) uses active focusing of elements to achieve beam forming and improved imaging capabilities. The recent approach involving FMC/TFM improves this method using synthetic reconstruction approaches. In this research, we introduce a novel hybrid approach that combines active and synthetic focusing at specific angle configurations, utilizing an Eccentric Virtual Array of Source Aperture (EVASA) in the Phased Array Ultrasonic Testing (PAUT) method. Some of the key advantages of this EVASA approach include (a) transmission at a specific angle with focus at a certain depth (b) lower time for inspection, (c) improved imaging of defect shapes and sizes, and (d) improved imaging of defects in regions where it was hard to reach (e) inspect the defects in much thicker samples with ease compared to FMC/TFM. The EVASA method employs a custom algorithm where array elements transmit ultrasound waves with pre-calculated delay laws, achieving beamforming focused at eccentric virtual sources located beneath the transducer. These virtual sources will focus on particular depths and angles, which will send a high-amplitude wave into the material for inspection at much deeper defects. Once the desired set of eccentric virtual sources has been formed within the material, the synthetic algorithms of FMC/TFM can be used to image the regions of inspection into the material. The parameters for forming the virtual sources, including their number, aperture position in the probe, and coordinates within the material, are based on the propagation angle and focal depth, extending up to the near-field distance. Comparative evaluation of EVASA with FMC/TFM shows its significant advantages, using metrics like Signal-to-Noise Ratio (SNR) which show substantial improvements. Importantly, EVASA is implemented using standard PAUT probes and instruments, requiring only customization in focal laws and reconstruction algorithms.
Full Waveform Inversion for High-Resolution Imaging and Defect Detection in Composites: On the Choice of Misfit, Regularization, and Parameterization
Detecting defects in materials and obtaining high-resolution images of the materials’ internal structures are essential for ensuring quality and structural integrity. Nondestructive testing (NDT) is favored for these purposes, thanks to its economic and environmental advantages. Among the various NDT techniques, ultrasound imaging shows great promise but entails several technical challenges, such as reliability and resolution. Full waveform inversion (FWI), an advanced optimization method, can generate high-resolution images of a material’s internal structure and identify defects. In principle, FWI minimizes the misfit between recorded ultrasonic signals and simulated ones, often through iterative optimization methods. However, the fact that FWI is highly nonlinear often hinders the reliability of the reconstructed images. This work explores different strategies to improve FWI for both high-resolution imaging and defect detection, focusing on defects such as delamination in composites. More specifically, it emphasizes the choice of misfit measures, regularization methods, and parameterization techniques; studies their impact on FWI improvements; and provides suggestions based on the resulting findings. Various misfit measures—including the L2- norm, L1-norm, cross-correlation (CC), and envelope measures—are compared in order to assess their effectiveness in improving FWI outcomes. Furthermore, different regularization methods were investigated for stabilizing FWI and enhancing its accuracy. Simple regularization methods (e.g., Tikhonov regularization) were compared with more advanced techniques (e.g., total variation regularization). Also, different parameterization strategies and their impacts on FWI performance were examined. By applying model constraints through reparameterization techniques, such as using Sigmoid transformations of model parameters and projecting them to expected ranges, this work aims to improve reconstruction accuracy and convergence. These findings suggest that CC functionals augmented with total variation regularization, while also applying Sigmoid transformation to the model parameters, is a particularly effective strategy for FWI in multilayered systems/materials with sharp-edged defects (e.g., delamination in composites.
Defects Recognition of High-Density Polyethylene Pipe Butt-Fusion Joint via STSVD Filtering Total Focusing Method and Machine Learning
The butt-fusion joints of high-density polyethylene (HDPE) pipes represent a critical vulnerability, being prone to various defects that potentially lead to structural failures. Therefore, it is essential to conduct nondestructive testing (NDT) on HDPE pipe butt-fusion joints for defect detection. This paper proposes a spatiotemporal singular value decomposition filtering total focusing method (STSVD-TFM), combined with machine learning (ML) to utilize A-scan signals for defect detection. Initially, the signal data is filtered using the STSVD method. Subsequently, feature parameters, including time domain features and spectral features, are extracted from the filtered data, and significant features are selected based on the Relief-F algorithm. Finally, the filtered signal data is employed for TFM imaging, with defect types determined by the training results of the ML models applied to the feature parameters. Detection experiments are conducted on HDPE pipe butt-fusion joint specimens, which included through-hole and square groove defects. The results demonstrate that the proposed method effectively reduces the amplitude of static clutter in the near-field areas, enhances the signal-tonoise ratio (SNR) of the detection images, and achieves high accuracy autonomous recognition of defect types.
Method for Tracking the Evolution of Fatigue Cracks in Rail Turnouts Using Ultrasonic Guided Waves
Rail turnouts are an essential component of railway systems, and rail bottom is prone to fatigue cracks under long-term exposure to heavy loads, which can lead to structural failure and pose a serious threat to train operation safety. Since fatigue crack propagation is nonlinear and concealed, early detection is of great significance. Ultrasonic guided waves, due to their advantages of long propagation distance, low attenuation, and ability to detect concealed areas, are widely used in structural health monitoring. However, during the early stages of crack propagation, the cracks are extremely small, and the signal features can be easily obscured by noise, significantly complicating reliable detection. This paper proposes a virtual sensor-based anomaly feature extraction method, in which ultrasonic guided wave signals are segmented into sliding window subsequences serving as multiple virtual sensors. By combining temporal features and fluctuation patterns, the anomaly weight of each subsequence is evaluated to identify potential abnormal regions. Validation through a switch rail fatigue loading experiment demonstrates that the proposed method can effectively track crack development trends at the early stages, adapt to complex signal scenarios, and significantly improve the accuracy and reliability of structural health monitoring.
Baseline-Free Defect Detection in Curved Surfaces Using a Neural Surrogate Wavefield Model
Accurate defect localization in structural health monitoring (SHM) can be performed by using high-resolution wavefield data, which can be challenging to obtain due to hardware limitations and time constraints. In this work, we propose a data-driven surrogate model that reconstructs high-resolution wavefields from sparse measurements, enabling efficient and precise defect localization. Our approach leverages a coordinate-based neural network to interpolate waveforms continuously, allowing for theoretically infinite resolution wavefield predictions. The proposed framework reduces data acquisition demands while preserving the fidelity of wavefield information, making it an efficient tool for SHM. Modeling results using numerical simulation data demonstrate that our approach reconstructs wavefield data and can be used to identify scattering sources associated with defects.
Noncontact Laser-Induced Ultrasound for Composites Inspection: Approaches for Material Discontinuity Visualization
Pulsed laser techniques enable non-contact generation of ultrasound in composite materials and can provide full-wavefield data. Nonetheless, since a pulsed laser provides a broadband excitation of acoustic waves, the resulting field contains both the dominant propagating modes and the weaker signals caused by interactions with possible internal defects such as delaminations. Hence, proper analyses of wavefields become essential to reveal subtle phenomena that can be masked by the primary wavefronts. In this study, the Singular Value Decomposition is applied to the measured wavefield to effectively separate and suppress the main wave components, whereas time-integrated maps are computed to enhance the discontinuity visualization. Experimental results from a composite plate with artificial interlaminar defect demonstrate that the adopted processing approaches improve defect contrast and facilitate more accurate localization of subsurface delaminations.
Anomaly Detection in Bridges Using Interferometric Synthetic Aperture Radar (InSAR)
Structural Health Monitoring (SHM) plays a crucial role in supporting decision-makers by providing vital information on the structural integrity of bridges. Traditional SHM systems typically rely on in-situ sensors, which can be time-consuming to install and vulnerable to failure during extreme weather events. In contrast, Synthetic Aperture Radar Interferometry (InSAR), leveraging active radar sensors mounted on satellites, has emerged as a promising remote sensing alternative. This technique enables the monitoring of any structure visible from the satellite’s orbit, eliminating the need for on-site sensor installation. Furthermore, depending on the availability of satellite imagery, InSAR allows for retrospective analysis of structural behavior. Historical displacement data can be used to detect anomalous trends, potentially serving as an early warning for hazardous structural conditions. This paper presents an anomaly detection procedure based on InSAR displacement time series. The proposed method is applied to a riverine bridge that collapsed in 2023. The displacement datasets are sourced from the European Ground Motion Service (EGMS), which utilizes Sentinel-1 satellite imagery.
Integrity Monitoring of Right-Angle Structures Using Metamaterial Assisted Guided Wave Mode Discrimination
This study proposes a novel method for structural integrity monitoring of right-angle structures, such as welded joints and T-profile panels, using ultrasonic guided waves. Firstly, building on the observed phenomenon of transmission and reflection selectivity of guided wave modes at geometric discontinuities, a framework for discontinuity detection is developed. Secondly, to address the issue of the damage-sensitive mode wave packets being immersed by other wave mode energy, a metamaterial-based mode selection design is proposed. The designed metasurface with a distinct bandgap selectively filters and amplifies certain guided wave mode (S0), making it feasible to recognize the signal caused by small-scale defects within a single measuring point. Thirdly, a testbench based on a T-shape aluminum profile is constructed, with the experimental results confirming the effectiveness of this approach in improving damage detection accuracy. By integrating the two predesigned damage index and introducing the Huber regression estimator, structural integrity can be evaluated precisely. The proposed framework contributes to advancing SHM techniques, particularly for structures with challenging geometries, by combining ultrasonic metamaterials with guided wave mode discrimination.
Development of Pedestrian and Structural Safety Monitoring System for Cable-Supported Pedestrian Bridges
A number of cable-supported pedestrian bridges have recently been constructed or are planned in Korea, primarily by local governments. These structures include cablestayed and suspension bridges, with or without pylons. Some are intentionally designed to sway. However, the structural behavior of these bridges is not fully understood, and monitoring system have not been implemented, unlike for other road and railway bridges. This ongoing research involves the development and application of various sensors and monitoring systems to assess pedestrian and structural safety on a representative pedestrian bridge. Long-term monitoring data are being collected to investigate structural behavior and performance. To measure displacement and movement, a GNSS (Global Navigation Satellite System) was installed at the midspan and anchorage blocks. Using latitude, longitude, and altitude data with RTK (Real-Time Kinematic) correction, sag as well as static and dynamic displacements are calculated in both horizontal and vertical directions. Threeaxis acceleration data are used to determine vibration frequencies and damping ratios, revealing the bridge’s dynamic characteristics. Additional parameters—such as deck and anchorage inclination, cable temperature, and wind direction and speed—are also monitored to ensure structural safety. For pedestrian monitoring, camera-based AI technologies are employed to estimate pedestrian loads and detect abnormal behaviors of pedestrians. Object recognition and tracking algorithms estimate the number and locations of pedestrians, while pose estimation combined with LSTM (Long Short-Term Memory) networks detect behaviors such as intentional cable shaking or leaning over railings. All monitoring components are integrated into an IoT system equipped with an embedded AI computing platform. The results of this research are expected to provide a practical remote monitoring system for pedestrian bridges and serve as a basis for establishing design and evaluation criteria for cable-supported pedestrian bridges.
Vibrational Monitoring of Isolated Targets Using Single-Pass SAR Images
This paper presents a method for extracting dynamic information from isolated vibrating targets using a single high-resolution X-band SAR image. The algorithm is based on the micro-motion principle to detect the movement of reflective targets. The method was validated through experimental tests conducted in Trento (Italy) and Glasgow (UK). The results obtained confirm the effectiveness of the algorithm in extracting velocities as low as 0.01 m/s.
Application of LSTM-Based GNSS Data Imputation for Structural Health Monitoring: A Case Study of a World’s Top 10 Longest Long-Span Floating Bridge in Norway
This paper investigates an LSTM-based method to impute missing GNSS data from the Bergsøysund Bridge in Norway. Simulating 10% randomly data loss, the approach demonstrated high quality in restoring time-series displacement measurements, supporting improved structural health monitoring. Future work will address longer time gaps for the data loss, and integration with additional sensors.
SHM of RC Bridge Girder for Damage Identification and Localization Using Machine Learning
Structural Health Monitoring (SHM) is essential for ensuring the safety of reinforced concrete (RC) bridge girders, which are key components of transportation infrastructure. Traditional methods like visual inspection and nondestructive evaluation can be expensive and may not cover all areas, making it hard to detect and localize damage comprehensively. This study uses Finite Element (FE) modelling in Abaqus software to create detailed computer models of scaled down RC bridge girder (as a RC beam) and simulate different localized shear crack scenarios through smear crack modelling to extract time history output with respect to acceleration data. These generated synthetic datasets are then used to train a multi-layer perceptron (MLP) based machine learning architecture for damage detection and localization. The MLP model is designed to automatically learn complex, non-linear relationships from a wide range of statistical, frequency domain, and wavelet features extracted from the acceleration data. This approach offers a cost-effective way to monitor bridges by reducing the need for extensive sensors and manual inspection, potentially improving safety and reduce maintenance costs. However, further validation with lab tested data and then scaling it to real-world data is needed to confirm its effectiveness.
GPS-Synchronization of a Seismic Accelerometer and a Ground-Based Interferometric Radar for Structural Health Monitoring
This paper presents a seismic accelerometer with GPS-synchronization based on the pulse per second (PPS) signal provided by the GNSS satellite network. This feature is essential for a Structural Health Monitoring (SHM) network that includes different sensors as for example accelerometers, cameras and a ground-based radar. The PPS signal is widely used in applications of timing synchronization because of its high accuracy and long-term stability. The common time-base between devices allows the internal time of the devices to be resynchronized every second, avoiding drift. In this work, we developed and tested acquisition board for seismic accelerometer with custom firmware designed to provide synchronized data recorded by different acquisition systems. The universal time (UTC) synchronized with PPS has been assigned to the data of each devices. The developed board has been tested with simultaneous acquisition a seismic accelerometer in MEMS (Micro Electro-Mechanical System) technology and a ground-based interferometric radar,both GPS synchronized. The two sensors are equipped with high precision GNSS receiver u-blox F9, that provide the UTC data and the PPS signal. To validate the synchronization system, the accelerometer and the ground-based radar were operated simultaneously to detect the oscillations of a moving target (a metallic swinging rod). The results of the experiment demonstrate that the sensors are well synchronized and the evaluations of the displacement are very similar, with good accuracy for performing dynamic structure monitoring. It is worth noting that this comparative analysis has highlighted the role of the automatic synchronization.
Automated Crawler-Based Ultrasonic Investigation to Monitor the Structural Integrity of Composite Liquid Hydrogen Tanks
Inspection of composite liquid hydrogen tanks (LH2) for aviation is very challenging using existing non-destructive testing (NDT) technology. This is due to the external insulation layer and limited access to the inside of the tank. New inspection technology is urgently needed for the LH2 tank inspection and maintenance of these LH2 tanks. This work presents the development of a compact high-payload ratio ultrasonic inspection automatic robotic crawler designed for the inspection of the inner curved surfaces of a carbon fiber reinforced polymer (CFRP) composite. The robot is equipped with wheeled locomotion and uses wheel encoders for precise localization on the irregular inner surfaces of the composite LH2 tank. The crawler carries an ultrasonic flaw detector payload housed in a specially designed enclosure, which securely holds a 5 MHz wheel probe for ultrasonic inspection. This study investigates Proportional- Integral-Derivative (PID) tuning for a crawler integrated with an ultrasonic phased array probe, executing straight-line motion on a CFRP composite surface under dynamic conditions at various speeds. The system is designed to operate through tank openings as small as 250-300 mm in diameter, providing a practical solution for challenging inspection scenarios of the LH2 tank.
Identifying and Locating Earthquake-Induced Damage in a High-Rise Using Neural Operators
Rapid response after earthquakes is vital to mitigate the effects of catastrophic structural failures and to save lives. In particular, critical infrastructure relies on accurate yet low-latency damage detection to facilitate timely responses. Although traditional techniques rely on hand-crafted feature extraction and data interpretation to identify the presence of damage within a structure, deep learning has resulted in newer methods that combine feature extraction and data interpretation in one. However, these models often require exorbitant amounts of data to train, are often computationally expensive, and are difficult to cross-validate with established theory due to their black box nature. In this paper, we present our current workflow that addresses the aforementioned challenges: (i) constructing representative finite-element models of a 15-story steel frame building with simulated damage pattern scenarios in the form of weld fractures, (ii) generating a synthetic waveform dataset for earthquake-induced dynamic response using a well-known dynamic analysis simulator, OpenSees, for the finite-element models and (iii) implementing a deep learning model that performs damage identification in addition to damage localization, i.e., locating the damage. To construct the dataset, we apply recorded earthquake ground motions to the 15-story building model modified to include small-scale damage scenarios imposed at beam-column connections. We then compute accelerations at each of the four corners within the structure for each individual modified model and earthquake input. Our deep learning model consists of a neural operator in conjunction with a machine learning mechanism called self-attention. To our knowledge, our work is the first work exploring the use of neural operators for earthquake-specific vibration-based damage identification. In addition, we present an analysis of our model performance and model fit, and compare our model to a subset of machine learning techniques.
Comparative Evaluation of Robot and Portable SHM Platforms for Drive-By Bridge Modal Identification
Bridge infrastructures lack affordable network-wide monitoring, as fixed S HM is typically limited to landmark spans. Drive-by monitoring with mobile robots offers scalable coverage using vehicles as moving sensor platforms. This paper experimentally evaluates a frequency domain subtraction algorithm on vibration data collected by an unmanned ground vehicle (UGV), validated against a portable SHM system on the UCF footbridge and the Alafaya Trail Bridge. The paired on-bridge and adjacent-road vibrations are transformed by FFT, scaled by energy and subtracted to isolate the dynamic response of the bridge and reveal modal peaks. On the footbridge, the UGV consistently recovered four of eight modal frequencies while on the stiffer Alafaya bridge, it identified the fundamental mode within 0.35% of the reference. Though portable SHM yields higher fidelity, it lacks network f easibility. UGV-based monitoring achieves a practical balance of accuracy and consistency. Future work will address uncertainty quantification, advanced modal frequency extraction algorithm and mode-shape extraction.
Uncertainty-Aware Autonomous Robotic Inspection Based on Active Vision and Deep Reinforcement Learning
This paper introduces an uncertainty-aware active vision framework, UADS-DRL, for autonomous robotic damage inspection. Unlike conventional methods that rely on passive raster scanning or prior learning-based agents, UADS-DRL formulates the task as a partially observable Markov decision process, enabling dynamic viewpoint selection guided by segmentation uncertainty. The agent leverages uncertainty cues to fuse predictions from multiple time steps, thereby achieving robust segmentation. Experimental results on photorealistic metallic surface inspection show that UADS-DRL achieves more than 2 times improvement in damage mIoU and over 50% reduction in inspection time compared to dense overlap raster scanning. It also surpasses the earlier ADS-DRL baseline by 20% in damage IoU with comparable inspection time. Given similar IoU performance, the proposed UADS-DRL agent can cut the inspection time by more than half compared with the previous learning baseline. These results underscore the effectiveness of integrating uncertainty estimation for accurate and time-efficient robotic inspection.
A Physics-Informed Multi-Source Domain Adaption Framework for Label-Free Post-Earthquake Structural Damage Assessment
Efficient and intelligent assessment of post-earthquake structural damage is critical for rapid disaster response. While data-driven approaches have shown promise, traditional supervised learning methods rely on extensive labeled datasets, which are often impractical to obtain for damaged structures. To address this limitation, we propose a physics-informed multi-source domain adaptation framework to predict post- earthquake structural damage for a target building without requiring damage labels. The multi-source domain integrates actual damage data and numerical modeling data from buildings similar to the target structure. The framework operates through three key steps. First, the similarity of key physics from each domain are analyzed to form a weight matrix, which enhances domain differentiation. Second, features from the multi- source and target domains are extracted and fed into a classifier and a discriminator. The classifier ensures that the features are damage-sensitive and accurately assign damage states, while the discriminator enforces that the features remain domain-invariant. Finally, the key parameters matrix is applied as weights during adversarial training to optimize the contribution of features from each source domain. The proposed framework provides a robust solution for assessing structural damage in scenarios where labeled data is scarce, significantly advancing the capabilities of post-earthquake damage evaluation.
Value of Information Across Ground Motion Models
Effective seismic emergency management depends on accurate seismic hazard analysis, where selecting a suitable Ground Motion Model (GMM) for the target region is a critical initial step. Although numerous GMMs are available, identifying the one that better captures the intensity measures characteristic of a target region remains nontrivial. This study investigates the effect of GMM selection on the Value of Information (VoI) obtained from ground motion monitoring. A case study is conducted for a bridge, considering multiple potential locations in Central Italy, with seismic demand estimated using two different GMMs applicable to the region. The results reveal that the difference in seismic hazard predictions between the two GMMs significantly affects the identification of the optimal emergency management actions and therefore the VoI. Using Log-Likelihood (LLH) method, to assess the statistical fit of each GMM against observed ground motions, one model is found to provide a better fit and a correspondingly higher VoI. These findings suggest that employing a statistically robust GMM can help avoid unnecessary infrastructure shutdowns and minimize the risk of underestimating seismic damage.
Experimental Validation of UAV-Deployed Edge Sensors for Frequency-Based Bridge Damage Detection
This work presents the experimental validation of a UAV-deployable wireless sensor package capable of edge-based Fast Fourier Transform (FFT) processing for frequencybased Structural Health Monitoring (SHM). The sensor system executes onboard computation to detect shifts in natural frequencies, enabling rapid assessment without relying on continuous high-bandwidth communication. A lab-scale beam structure is used to simulate structural state changes by repositioning support locations, with a UAVdeployable sensor placed on the beam to capture vibration data alongside a reference accelerometer. The embedded FFT algorithm performs local signal transformation and peak detection, successfully identifying modal frequency shifts correlated with altered boundary conditions. These peak frequencies are logged and prepared for selective wireless transmission to reduce power and data transmission demands. The sensor package is shown to reliably detect frequency shifts of approximately 1 Hz across configurations. This demonstration underscores the viability of low-power, autonomous edge-sensing systems for scalable, rapid-deployment SHM platforms, particularly when structural access is constrained or latency is critical.
Impact of Categorical Feature Encoding on Machine Learning Based Shear Strength Prediction
Recent advancements in machine learning (ML) offer powerful tools for predicting the structural integrity of civil infrastructure. A critical yet often overlooked aspect of ML modeling is data preprocessing, particularly categorical feature encoding. This study examines how different encoding schemes for interface conditions (monolithic, rough, smooth) impact ML predictions of interfacial shear strength. It compares categorical encodings (one-hot and five label encoding variations) with numerical friction coefficients (monolithic = 1.4, rough = 1.0, smooth = 0.6) across four ML models: eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Regression (SVR), and Artificial Neural Network (ANN). Among 28 models, categorical encodings outperform numerical representations in 88% of cases, with ensemble models (RF, XGBoost) proving robust yet RF more sensitive to encoding variations. SVR and ANN exhibit encoding-dependent performance, with some SVR models achieving 12% higher accuracy than numerical- based models. Findings emphasize the crucial role of encoding choices in ML model performance, advocating for adaptive preprocessing techniques to enhance reliability in structural engineering and beyond.
Advanced NeRF (ABM-Nerfacto) for High-Definition Digital Twin and Damage Mapping
Since 2017, extensive damage detection using advanced deep learning models and computer vision techniques has been actively explored. However, efficiently mapping detected damage in 3D digital twin model remains a challenge, as few studies have successfully integrated deep learning and computer vision for precise damage representation. To address this gap, this study investigates an enhanced NeRF-based model, ABM-Nerfacto [1], designed for high-definition and efficient damage mapping in 3D digital twin. This advancement facilitates more effective structural health monitoring, infrastructure maintenance, and a comprehensive pixel-level overview of damage distribution. The Nerfacto model was extensively modified and integrated with an advanced attention mechanism to improve its ability to learn structural features and damage patterns. When applied to a bridge system, the developed model demonstrated exceptional accuracy in pixel-wise damage mapping, successfully generating a highfidelity 3D digital twin.
Monitoring Structures with Teams of Mobile Robots
This paper addresses the problem of monitoring structures with potential emergent damage through adaptive sensing provided by teams of mobile robots. Advantages of mobile robot teams for structural health monitoring include: 1. Multiple views of a given structure, 2. Adaptive movements that focus attention in response to observed conditions,3. Heterogeneous sensing and movement, and 4. Federated health monitoring and prognosis assessment through networked sharing and processing of information. Towards this end three cases of the use of mobile robot teams will be presented: 1. Heterogeneous robot teams for home and small building maintenance – Identifying, diagnosing and mitigating damage to homes and small buildings is a vexing set of problems for the owners. As an aid small controlled bristlebots and quadruped robot dogs (QRDs) carry sensors throughout a small building, assess conditions, provide prognoses and networked links to repair options; 2. Culverts are primary components of stormwater and flood prevention infrastructure. Inspecting small culverts is difficult for humans and large culverts are accessible but dangerous due to issues of confined spaces. Low-cost mobile robots have emerged as a competitive inspection option for accessible culverts with straight or short runs that permit wireless telemetry. Longer culverts and those with bends, branches and drop inlets pose challenges to the telemetry. Teams of robots extend the range of inspection through multi-hop video and control telemetry; 3. Ground penetrating radar (GPR) is a method of sensing subsurface infrastructure conditions with high-frequency electromagnetic waves. Conventional GPRs operate in a suboptimal monostatic or bistatic mode, are tedious to operate and have limitations in sensing congested utility subsurface conditions. Coordinated multi- static ground penetrating radar operated with mobile robot teams alleviates some of these concerns and provide better subsurface assessments with automated methods that focus attention on subsurface features of interest. Results from laboratory and field tests of these robot teams, as well as organizing principles of control and automated information processing are presented.
Comparative Analysis of Simulation Models for Digital Twins and Damage Identification on Bridges: A Steel Bridge Case Study Using Strain Measurements
Structural Health Monitoring (SHM) and Digital Twin (DT) technologies are transforming the management of civil infrastructure by enabling real-time condition assessment and predictive maintenance. A key component of these advancements is the development of accurate simulation models that represent physical assets in virtual environments. This paper explores different simulation approaches for DTs and damage identification on bridges, using a steel bridge with an orthotropic deck as a case study. Various state-of-the-art modeling techniques, including truss models and finite element models, are developed based on the bridge’s as-designed geometry. Their performance is evaluated in terms of computational efficiency, ease of parameterization, and accuracy in capturing structural behavior, with strain measurements from a load test serving as a reference. Statistical metrics assess the alignment between simulated and observed data. While all models involve certain simplifications, they demonstrate good agreement with experimental results. The findings highlight the importance of selecting an appropriate modeling approach based on specific SHM and DT objectives, such as real-time monitoring or detailed structural analysis. While truss models offer advantages in computational efficiency and straightforward parameterization, more detailed finite element models provide higher fidelity for local structural behavior. This study underscores the potential of even simplified models to achieve reliable predictions of global structural behavior when key geometric features are appropriately represented. Ongoing work further refines these approaches to enhance their applicability in damage identification, contributing to the advancement of DT-based predictive maintenance for bridges.
Track-Bridge Interaction Effects in Ballast Superstructures: Large-Scale Experiments as a Basis for SHM-Driven Bridge Models
Railway bridges are crucial key components of railway infrastructure. The steady increase in traffic volume (intensification of train traffic along with increasing axle loads) poses a major challenge for infrastructure operators in ensuring the resilience of existing structures and assessing their service life realistically and economically. The application and combination of digital twins and structural health monitoring (SHM) are essential for mastering these challenges. A realistic assessment and prognosis of railway bridge conditions and performance requires detailed information on the actual system parameters. In this context, the computationally implemented interaction between the track and supporting structure significantly influences the generated results and the resulting condition assessments of the railway bridge system. A wide range of mechanical modelling options with different levels of complexity are available for implementing track-bridge interaction (TBI) effects in computational models. However, the considerable range of model-related stiffness and damping parameters makes it particularly more challenging to consider TBI effects realistically in practical applications. Based on this background, the research activities of the Institute of Structural Engineering at TU Wien focus on the experimental investigation of TBI effects in ballast superstructures. This contribution presents the key results of the investigation of vertical and longitudinal TBI effects. Using two large-scale test facilities, the interaction effects occurring in ballast superstructures can be investigated separately in both the vertical and longitudinal directions in an isolated manner. The test setup allows the stiffness and damping properties of the ballast superstructure to be precisely determined, taking into account time-dependent and environmental influences. The overarching goal is to generate a holistic and physically based mechanical model with precisely defined stiffness and damping characteristics for describing TBI effects, which can be implemented as simply as possible in corresponding digital bridge models. This contributes significantly to the reliable condition assessment and condition prognosis of railway bridges.
Monitoring of Civil Engineering Structures Based on H∞ Estimation
Subspace methods, a field of numerical mathematics, are applied to system identification of mechanical structures. The efficacy of these methods, applicable to both deterministic and stochastic model estimation, has been proven on numerous occasions in practical and research applications. The system identification process yields multidimensional state space systems, which serve as the mathematical foundation for the models. In the context of stochastic system identification for mechanical systems, particularly those exposed to ambient noise sources such as wind and traffic, output-only methods are employed. Since the system inputs are unknown, the model can’t be completely determined. By applying ?∞ optimization methods, models with a theoretical normalized input are obtained. These state space systems can be interpreted as a digital twin at a designated time point. In this contribution the method “Autonomous Model Order Selection” (AMOS) is linked with the structural health monitoring (SHM) method “State Projection Estimation Error” (SP2E), which leads to an automatic and continuous SHM approach. The output of the SHM method is a damage localization indicator, which is combinable to a digital twin. This approach is verified with laboratory measurements and a large-scale experiment on a real bridge.
Hybrid Workflow for Digital Twin Creation: Human-in-the-Loop Segmentation and Geometry-Aware Modeling of Bridge Structures
Reliable as-is modeling of existing infrastructure is a cornerstone of structural health monitoring and digital twin creation. While data-driven segmentation approaches have advanced automation for common bridge types, they often struggle to generalize to atypical structures. This paper instead proposes an interactive segmentation and modeling technique that aims to reduce the time needed for producing accurate geometric models and segmentation masks by considering both fine local features as well as large-scale geometry. Our method begins with a graph-based oversegmentation of the input point cloud, compressing millions of points into a superpoint graph optimized for real-time interaction. On this graph, we formulate user-guided segmentation as an energy minimization problem, incorporating both local features and region-growing cues to propagate sparse user input. Once segmented, each component undergoes geometry-specific processing: planar elements are modeled via RANSAC-based decomposition and intersection analysis, while irregular surfaces are reconstructed using adaptive triangulation informed by local point density.The resulting surface representations are converted into IFC-compliant building elements, enabling integration with existing BIM systems. We evaluate the method on the Nibelungen Bridge in Worms, Germany, achieving over 99% segmentation accuracy with minimal user input and a final geometric RMSE of 5.8 cm.
Smart Bridges Will Solve Our Bridge Crisis
Bridges are a large potential market for Structural Health monitoring (SHM), but SHM has had very little market penetration. Yet at the same time there exists a growing bridge crisis. At the current rate of investment, it will take until 2071 to make all the repairs that are currently necessary, and the additional deterioration over the next 50 years will become overwhelming. The nation needs a systematic performance-based program for bridge preservation, whereby existing deterioration is prioritized, and the focus is on rehabilitation and preventive maintenance. The approach the USA is taking to Bridge Asset Management is not working and needs to change if we are to improve and not degrade our transportation system. The industry needs to innovate to solve the problem resulting in increased productivity or in the case of bridges lower life cycle costs while maintaining full functionality. Structural Health monitoring (SHM) has been used for many years but has not gained widespread adoption due to it being a partial solution to the bridge asset life cycle management problem and uneconomical to implement. Industry consensus is that Generation 1 SHM alone will not solve the bridge crisis. What is needed is a Smart Bridge using a total solution provided by a digital enterprise bridge performance management platform based on an IoT/cloud architecture that includes: Structural and Operating performance measurement and monitoring, Real-time analytics and alarms, and fact-based decisions for improved bridge life cycle performance and economics. This paper will present the result of implementing a Smart Bridge strategy on a fleet of bridges by NYSDOT USA and Manitoba Canada. Their success with Smart Bridges has led to a comprehensive bridge lifecycle management strategy aimed at creating 150- year-old bridges through performance measurement and rehabilitation. This strategy is projected to save hundreds of millions of dollars over 20 years. Smart Bridges are crucial for modern smart cities, integrating digital technologies to optimize bridge performance, reduce congestion, enhance public safety, and support autonomous vehicles. They offer a transformative approach to bridge asset management, supplementing outdated visual inspection methods with advanced IoT technology and data analytics. Both NYSDOT and Manitoba Infrastructure exemplify best practices in innovative bridge asset management, demonstrating the significant benefits of Smart Bridge strategies. These initiatives provide a blueprint for other regions to follow, ensuring the sustainability and reliability of critical bridge infrastructures for future generations.
Investigating Robustness in Detection, Localization and Compensation of Sensor Malfunctions in Degrading Structural Health Monitoring Systems
Structural Health Monitoring (SHM) systems are essential for damage detection and maintenance planning in aging infrastructure. However, sensor degradation increases epistemic uncertainty and leads to incorrect SHM assessments, making it necessary to develop robust methods for handling sensor faults. This work investigates the robustness of a recently proposed framework for detecting, localizing, and compensating faulty sensors. The approach uses the Mahalanobis distance between sensor data frames from a current and a reference period, relying on cross-validation among sensor outputs. It includes a so-called α-level mapping, which links evaluated distances to corresponding levels of uncertainty relying on common statistical thresholds, as well as an algorithmic mechanism for excluding faulty sensors based on individual trust scores. The robustness study, using artificial FEM data from a steel lattice mast, identifies three key parameters that primarily govern the correct classification of faulty and healthy sensors over time. Results from the case study show that the method remains effective under multiple sensor faults, and identifies a favorable parameter domain, yielding the optimal performance. These findings support practical parameter recommendations to maintain confidence in SHM systems during long-term operation.
AI-Powered Digital Twins of Concrete Bridges for Enhanced Asset Management
Ensuring the long-term sustainability of bridge assets is crucial, but manual inspections are often time-consuming, laborious, and can be hazardous. These issues can be addressed by leveraging Artificial Intelligence (AI) with Digital Twin technology for enhanced monitoring and maintenance. This approach automates defect identification, reduces field time, and simplifies generating repair documentation, shifting from traditional to proactive, data-driven asset management. In this study, the condition assessment of concrete bridge decks involves three steps: data acquisition, interpretive analysis, and visualization. Autonomous Unmanned Aerial Vehicles (UAVs) are utilized to capture high-quality images of the bridge, which are then stitched into 3D models using photogrammetry. Deep Learning algorithms detect defects such as cracking and spalling in these images, significantly reducing manual inspection time. The Digital Twin is visualized in an Asset Management Portal (AMP), providing stakeholders with a clear, interactive representation of the bridge's condition. This enables timely interventions and historical tracking of defects. By leveraging UAVs, photogrammetry, and ML/AI, the transformative potential of Digital Twins in Structural Health Monitoring can be fully realized.
Damage Identification Strategies for the Preservation of an Existing PC-Bridge
This paper explores the application of Structural Health Monitoring (SHM) as a decision-making tool for the preservation of the Nibelungen Bridge in Worms (NBW), a prestressed concrete (PC) bridge completed in 1953. The NBW exhibits damage mechanisms that are characteristic of PC-structures of that era, including corrosion of tendons and inadequate shear load-bearing capacity. To address these issues, an initial SHM system was installed in May 2023. The present study proposes damage identification strategies aimed at preserving the NBW. By leveraging SHM data, the study explores both model-based and data-based approaches to ensure the long-term safety and functionality of this historic structure.
Collaborative Structural Health Monitoring for Bridge Digital Twins
Structural Health Monitoring (SHM) is an effective tool that not only reduces reliance on periodic inspections but also enhances them by analyzing the current state of a structure based on the latest structural data. Collaborative SHM, which integrates various SHM systems within the scope of bridge digital twins (BDTs), enhances infrastructure resilience and maintenance strategies. However, it faces challenges in integrating distributed sensor networks and requires interdisciplinary collaboration. In this work, various aspects of enhancing collaborative SHM with BDTs are presented. As a pilot project, the Nibelungen Bridge in Worms (NBW), Germany, is introduced. Based on specific stakeholder and project requirements, various SHM systems have been installed on this bridge. To address these challenges, goal-oriented solutions have been developed and elaborated. Finally, conclusions and future outlooks are presented.
Development of a Structural Monitoring System for the Digital Twin of the Hull Structure of an Ore Carrier
Ship structures are constantly subjected to wave loads and the excessive loads or repetitive loads increase a risk of failure. To avoid economic and environmental losses caused by the failure of ship structures, ship manoeuvring, maintenance, and design decisions are made so that the stress due to loads acting on the hull does not exceed the strength of the structure. However, because there is uncertainty in both the stress and the strength, there is the potential for safety and economics to be compromised. One solution to reduce the uncertainty is a digital twin that provides stress information at areas of interest in the ship structure in cyber space. In general, a digital twin is an integrated model that accurately reconstructs the state of a physical object based on sensing data measured in physical space and digital models that simulate the behavior of physical objects. Stress monitoring of the ship structure, including unmeasured parts, using the digital twin helps crews, shipping companies, and other stakeholders make rational decisions based on the situation. In this study, we developed a structural monitoring system for digital twins of ship structures with the aim of improving the safety and economic efficiency of ships. This system reconstructs and show the stress due to wave loads in a whole area of the hull structure. We developed a measurement module to obtain strain data from an interrogator of fiber optic sensors, an analysis module to calculate the stress at areas of interest based on the strain data and the Kalman filter, and a visualizing module with the graphical user interface (GUI) to show the stress of the hull structure, including the unmeasured parts. The analysis module also counts stress continuously and estimates the cumulative fatigue damage. These modules were integrated to form into the structural monitoring system for a digital twin. The structural monitoring system was implemented on an ore carrier in February 2023 and has been continuously operated without any serious troubles. In this paper, we introduce the modules with related technologies and the structural monitoring system with monitoring and operating data. This works is expected to provide valuable insights for building a digital twin for ship structures.
Enhanced Numerical Simulation and Experimental Validation of Dam Gate Trunnion Rod Anchorage Systems Under Varied Pre-Tensioning Conditions
This research presents a comprehensive Finite Element Analysis (FEA) approach to verify the testing configuration for trunnion rod anchorage assemblies used in dam gates, considering their relevant in-situ conditions. A substantial number of Finite Element (FE) models were constructed using Abaqus/CAE 2023, focusing on the effects of varying pre-tension levels and potential failure modes of trunnion rods. One hundred ninety-seven distinct models were developed, each capturing various rod failure configurations and pre-tensioning scenarios, while consistently maintaining the geometric, mesh, and assembly configurations across the different test cases. To enhance efficiency and ensure consistent testing across a wide range of configurations, a Python script was created to automate the model generation process. The resulting test plan comprised 57 configurations at a pre-stressing level of 15%, in addition to 35 configurations corresponding to pre-stressing levels of 30%, 40%, 50%, and 60% of the Guaranteed Ultimate Tensile Strength. A comparative analysis between the FEA simulations and experimental laboratory tests revealed a mean absolute percentage difference of 6.86% for the initial post-tension levels. The final post-tension levels obtained from the FEA simulations showed a mean absolute percentage difference of 10.99% compared to the laboratory results. The impact of the applied load is more pronounced at lower post-tension levels, particularly at 15%. In contrast, at a 60% posttension level, the influence of the applied actuator load appears minimal compared to the significant effects noted at the 15% post-tension level. The most substantial differences in tension levels between the laboratory testing and the FEA models primarily occur at the 15% pre-stressing level. These results confirm the reliability of the FEA models in predicting the structural behavior of trunnion rod anchorage assemblies, paving the way for future assessments of the remaining life of these assemblies.
Investigation of Robustness in Detecting and Localizing Sensor Malfunctions in Deteriorating Structural Health Monitoring Systems
A Framework for Enhancing Self-Generating Digital Twins with Hierarchical Models, Physics-Informed Machine Learning, Gen-AI, and Dynamic Diffusion Models for Real-Time SHM and Predictive Maintenance
This paper introduces a conceptual framework to advance Self-Generating Digital Twins (SGDTs) by integrating Hierarchical Digital Twin (HDT) architectures, Physics- Informed Machine Learning (PIML), Generative Artificial Intelligence (Gen-AI), and dynamic diffusion models. Current SGDT implementations face significant challenges in real-time Structural Health Monitoring (SHM) due to the complexity of processing vast, multi-dimensional data streams and the inherent difficulties in achieving accurate predictive insights across large-scale systems. The reliance on a singular SGDT framework often results in inefficiencies when addressing multi-level structural complexities and managing uncertainties, highlighting the necessity for a scalable and layered approach. The proposed framework leverages HDTs to decompose global and local structural behaviours into interconnected layers, simplifying computational demands while enhancing interpretability. By embedding fundamental physical laws and constraints into the learning process, PIML improves the efficiency and accuracy of Gen-AI models. Gen-AI autonomously refines the digital twin models, synthesising actionable insights from real-time data streams, while dynamic diffusion models facilitate precise damage evolution predictions under varying operational and environmental conditions. This proposal addresses critical gaps in real-time SHM by overcoming the limitations of standalone SGDT systems and introducing a methodology that is both scalable and adaptable. It offers a novel pathway for optimised resource allocation, reduced downtime, and improved sustainability in infrastructure management. Although validation through real-world application is a future objective, the framework provides a robust foundation for advancing SHM technologies to meet the growing demands of mobility, autonomy, and sustainability.
A Wireless Enabled IOT Nondestructive Fatigue Damage Sensor for Estimation of Remaining Useful Service (RUL) of Structures Through Artificial Intelligence-Machine Learning Algorithms
In this study, a wireless enabled novel non-destructive evaluation (NDE) fatigue damage sensor for the structural fatigue health monitoring and prediction of fatigue life of structural members of large engineering structures is presented. The smart predictive IOT (Internet of Things) fatigue damage sensor has special designed smart breakable sacrificial sensor beams for early fatigue damage detection and prediction of fatigue sensitive structural and mechanical parts of fatigue sensitive structures. The IOT non-destructive fatigue damage sensor can measure directly the state of fatigue damage accumulation levels and predict the remaining useful life (RUL) of structural components or locations for the life cycle management and predictive maintenance of structures without using any fatigue damage algorithm. The non-destructive evaluation (NDE) fatigue sensor consists of mechanical and electronic units. The mechanical part of the fatigue damage sensor has special designed smart and predictive sacrificial beams with different levels of lifetimes (10% N, 20% N, 30% N, 40% N, 50% N, 60% N, 70% N, 80% N, 90% N, 95-100% N) normalized to the total lifetime of a real structure. The mechanical sensor beams designed with special geometry using stress magnifying effects to measure the fatigue damage accumulating level and early fatigue damage detection and fatigue state health monitoring of structures. The smart fatigue damage sensors attached onto the surface on a real structure is expected to have the same cyclic stress-strain loading behaviour and the same cyclic stress-strain history for during the operational service life. The sensor having multiple parallel oriented and mini-micro non-destructive fatigue damage measurement sensing beams are designed to fail earlier than a real structure in the different predetermined fatigue lifetimes like a mechanical fatigue fusing system. Due to its predictive feature the fatigue sensor will extent not only the service life of fatigue sensitive mechanical components but also increase the safety and reliability of structures. Since the wireless enabled fatigue damage sensor network through Internet of Things (IOT) technologies collect the real operational fatigue data remotely, the statistical fatigue damage sensor network data can be used for the prediction of lifetimes of structural or mechanical members by using Artificial Intelligence (AI) Machine Learning (ML) Algorithms. The distributed fatigue sensor network also provides a real operational-experimental statistical data for condition based predictive maintenance, maintenance management, end of service life indicator (ESLI) and development of new fatigue design tools for fatigue sensitive parts or locations of fatigue critic metallic and composite structures.
Physically-Based Data-Driven Approach for Anomaly Detection in Long-Term Continuous Monitoring Systems: Application to a Case Study
Smart management of cultural heritage structures benefits from long-term continuous monitoring. Continuous monitoring collects large dataset of measurements that significantly improve the possibility to provide a correct interpretation of the structure’s behaviour and to detect evolutionary trends and anomalies. By referring to structural features which are systematically extracted from the monitored dataset and identified as Reference Quantities (RQ), a physically-based data-driven approach is developed in this research work and applied to the case study of the Two Towers in Bologna (Italy). The proposed approach streamlines data processing, correlates structural behaviour, accounts for environmental effects, and supports the establishment of alert thresholds for an effective structural monitoring for the considered case study.
Integration of BIM, Sensors, and Remote Monitoring Techniques for Structural Health Monitoring: A Platform for Verona Arena
This research emphasizes the importance of developing interoperable solutions that enable structural monitoring data to be directly comparable with other types of information and effectively utilized through Digital Shadow. The focus is on monuments of significant cultural and historical importance, which face threats from natural hazards such as earthquakes and environmental degradation, as well as human- induced stresses. These challenges highlight the urgent need for advanced tools and methodologies that enable the continuous and reliable assessment of their structural integrity, ensuring their preservation for future generations. This study presents a comprehensive and integrated approach to information modeling methods for data management, specifically tailored for monitoring and preserving culturally significant structures. The Roman Arena in Verona serves as the case study, demonstrating how Building Information Modeling (BIM) systems and sensor networks can be integrated to assess structural health and manage large datasets efficiently. The study explores the use of open data standards, such as IFC, to ensure interoperability and facilitate seamless integration of different data sources. It also highlights the application of SQL databases for handling real-time sensor data, enabling a dynamic and detailed digital shadow of the monitored structure. By combining cutting-edge digital tools, including BIM, remote sensing technologies, and environmental sensors, the proposed methodology provides a comprehensive system that not only facilitates real-time data analysis but also offers insights for conservation and restoration efforts. A framework that organizes monitoring data into an SQL database, which is subsequently linked to a specific database for Industry Foundation Classes (IFC) files is proposed. This linkage enables the real-time visualization and dynamic updating of the structure’s digital representation. An online platform is introduced to allow users to view and interact with the updated IFC model and query the database to monitor sensor data trends over time. This platform moves beyond traditional visualization tools by integrating monitoring data with actionable insights, offering advanced capabilities such as the identification of specific areas requiring intervention based on sensor readings. By providing decision-makers with a comprehensive view that combines real-time monitoring with historical and predictive data, the system supports more effective conservation and restoration strategies. This research demonstrates how modern data management techniques and digital tools can be leveraged to enhance the long-term preservation of cultural heritage assets. The proposed framework is scalable and adaptable, offering a model that can be applied to other heritage sites facing similar challenges. By offering a robust and flexible framework for the interoperable use of data generated through structural health monitoring, enabling seamless integration with other information types, this work aims to contribute to the global effort towards preserving cultural heritage in the face of growing environmental and societal pressures.
Bearing Fault Diagnosis Method for Single-Channel Vibration Signal
Conventional methods for diagnosing bearing faults typically depend on multiple sensors to monitor the operational state of equipment. However, in practical applications, space constraints often limit sensor placement, making it difficult to ensure diagnostic accuracy. This study proposes a fault diagnosis method based on the fusion of one-dimensional and two-dimensional features extracted from a singlechannel vibration signal. Firstly, one-dimensional time-domain and frequencydomain features are extracted from the vibration signal. The minimum Redundancy Maximum Relevance (MRMR) method is employed to select key features that have strong correlation with fault types while minimizing redundancy. Secondly, the single-channel vibration signal is converted into a two-dimensional image using the Gramian Angular Difference Field (GADF) method, and a Convolutional Neural Network (CNN) is employed to automatically extract high-level image features. The optimized one-dimensional feature vector is then fused with the two-dimensional image feature vector to create a high-dimensional feature representation, and the Support Vector Machine (SVM) is utilized to identify fault types based on the fused feature vector. The experimental results show that the feature fusion strategy using only single-channel vibration signals significantly improves the accuracy of fault diagnosis and provides an effective and interpretable solution for industrial fault diagnosis.
A Monitoring Framework for Multi-Fault Detection in Rotating Shafts Integrating Deep Learning and Signal Reconstruction
Shafts and rotors are essential components in many engineering applications, often playing a critical role in system functionality. Unexpected failures can result in costly downtime or pose risks to human safety, with both situations to be prevented. To mitigate these risks, there is a growing interest in monitoring systems that can proactively detect potential faults and provide real-time diagnostic insights to operators. However, rotor damage often leads to imbalances that can adversely affect other components, such as supports, or induce secondary faults, like cracks caused by high forces associated with rotation. Detecting both the imbalance and the presence of additional faults enhances the assessment of operational risks, enabling early alerts for catastrophic failures and safety-critical concerns. This study presents a monitoring framework for detecting multiple sources of damage in a transmission shaft by analyzing the accelerations at the support bearings during rotation. The rotor is experimentally tested on a test rig, with imbalance masses placed at various locations to simulate the effects of bullet impacts. In addition, a second damage source, specifically a support fault, is also artificially introduced and tested.. The proposed methodology combines a CNN for imbalance diagnosis with a signal reconstruction model for anomaly detection, both using the same acceleration data. This dual approach enables accurate localization of imbalances and detection of unknown faults while minimizing data requirements. The framework is designed to encourage future implementations in real-world applications potentially subjected to multiple faults.
Technical and Oversight Considerations for Reliable SHM Utilization
As the use of Structural Health Monitoring (SHM) systems continues to expand, the key elements associated with proper performance assessments and oversight requirements are becoming very well-defined. Similarly, supporting developments such as SHM guidance, regulatory measures, quality assurance and training are progressing to build a firm foundation for SHM deployment. Extensive SHM studies have highlighted the viable use of in-situ sensors for real-time health monitoring of a myriad of structures across multiple industries. SHM use can overcome inspection impediments stemming from accessibility limitations, complex geometries, and the location and depth of hidden damage. A critical aspect to applying SHM solutions is determining the operational requirements for SHM systems and the full array of variables that must be considered in the assessment process. In the case of the aviation industry, OEMs, airlines, regulators and researchers have worked together to define a detailed process for determining how to safely adopt SHM solutions. Multiple, formal SHM certification efforts have been completed to exercise and define the process of validating SHM systems. SHM certification must address the full spectrum of issues ranging from design to performance and deployment to continued airworthiness. This paper will provide an update on SHM deployment and certification in several aircraft applications, streamlined processes for airline adoption and recent FAA and industry initiatives to aid thorough evaluations of SHM systems. An FAA Issue Paper now provides essential guidelines for SHM system designers and procedures for assessing the performance of SHM systems. Formal documents have been issued by aircraft manufacturers to accommodate SHM usage. The Aerospace Industry Steering Committee for SHM has published multiple, essential standards for integrating and certifying SHM use on aerospace structures. These efforts are allowing SHM solutions to quickly and properly support maintenance activities while establishing policies and guidance to ensure the safe, uniform and comprehensive certification of SHM systems. Furthermore, this foundation will foster expanded regulatory and industry guidance as SHM efforts ultimately move towards Condition Based Maintenance programs.
Improve Aircraft Structure Safety by Reinforcing the Critical Location Using Structural Health Monitoring
Fatigue remains the predominant mode of failure in the aviation industry, accounting for 55% of the incidents occurring. While the current industry practice relies on attaching a doubler on one side to address fatigue crack growth, the induced bending stress due to the neutral axis offset can be counterproductive and accelerate the crack growth rate. In this work, the fatigue crack growth of an airplane panel was examined for different configurations including one and two-sided doublers. With the aim of producing a reliable and simplified model capturing the fatigue crack growth mechanism. The results showed that while one-sided doublers reduce the operational lifetime by 38%, using a two-sided doubler, it increases by 86%. Analyzing the peak von-Mises stress across the different configurations, the one-sided doubler configuration had the highest stress due to bending, while the two-sided configuration mitigated and addressed this design problem. The work motivates further development in studying the application of two-sided doublers as a new industry standard to significantly extend the operational lifetime of an aircraft while reducing the operating costs of aviation companies.
A Model-Based Localized Damage Detection in Structural Health Monitoring
A structural damage detection method is presented, labeled as Partitioned Damage Detection (PDD) method, which employs the recently developed displacement-only partitioned (DP) equations. The proposed PDD method starts with experimentally or simulation-generated modes and mode shapes. Second, it transforms the conventional assembled flexibility into DP-based partitioned flexibility. Third, the free-free forms of substructural flexibility is extracted from the DP-based flexibility. Fourth, Damage Indicator (DI) for each substructures are computed by the ratio of the traces of the free-free healthy and damaged substructural flexibilities. Numerical simulation and experiments show that the proposed PDD method correctly identifies both the damage location and damage levels for determinate structures. For indeterminate structures, the proposed PDD method correctly identifies the damage locations, while underestimating damage levels. A compensation scheme is developed for improving damage levels, which results in a reliable estimation of both damage locations and damage levels.
Vibration-Based Fault Detection in Belt Drive Systems Using FFT and LSTM Models
Fault detection in rotating machinery is essential for preventing costly maintenance, minimizing downtime, and ensuring operational safety. In this study, a belt drive system was analyzed for fault detection using vibration analysis with Fast Fourier Transform (FFT) and machine learning (ML). A custom test rig, consisting of a drive and driven pulley, was designed, and various fault conditions, including a loosened belt, misalignment, and mass imbalance, were intentionally introduced, along with a healthy baseline scenario. Vibration data were collected using a high-sampling- rate EnDAQ sensor, a high-quality data acquisition device. The signals were analyzed in both the time and frequency domains, revealing significant changes in vibration patterns and FFT spectra across different conditions. FFT analysis provided insights into the characteristics of each fault type. Additionally, a Long Short-Term Memory (LSTM) neural network was developed to classify the system state. The model achieved high accuracy (98%) in detecting and distinguishing between fault types. Overall, the results demonstrate the strong potential of FFT and ML techniques for effective fault detection in belt drive systems.
Cryogenic Temperature Effects on Guided Wave Structural Health Monitoring Signals in Unidirectional Carbon Fiber Reinforced Plastics
Structural Health Monitoring (SHM) is a promising technology for enhancing the safety and performance of high-value composite structures operating in extreme environments, such as cryogenic temperatures encountered in aerospace systems, hydrogen storage tanks, and emerging space applications. These structures are subject to significant mechanical and thermal stresses that can degrade material properties, increase brittleness, and threaten structural integrity. In such conditions, traditional inspection methods may be infeasible or insufficient, while SHM enables real-time, insitu damage detection and continuous monitoring throughout a structure's service life. Among SHM techniques, Guided Wave (GW) methods offer significant advantages for fiber-reinforced polymer composites due to their long-range propagation capability and sensitivity to various defect types, including delaminations, matrix cracking, and impact damage. However, GW signals are also influenced by environmental and operational factors such as temperature variations, mechanical loading, and adjacent media (e.g., moisture, frost, or liquid interfaces), which can complicate signal interpretation and potentially mask real damages or produce false positives. This study investigates the effects of cryogenic temperature and surrounding media on GW signal behavior in a unidirectional carbon fiber-reinforced polymer panel equipped with co-bonded DuraAct™ piezoceramic transducers. The specimen is exposed to controlled thermal cycling, including immersion in liquid nitrogen, and guided wave responses are measured across a wide frequency range. Results reveal distinct frequency and temperature-dependent behavior in both S0 and A0 wave modes. Observed changes in amplitude and time-of-flight are linked to transducer performance, material stiffening, and boundary media interactions. These findings highlight the critical need for environmental compensation in guided wave SHM systems and highlight opportunities to monitor both structural health and environmental conditions using wave behavior in applications spanning aeronautics, hydrogen energy systems, and space technologies.
Effect of Material Attenuation on Lamb Wave Propagation in a Solar Cell Array
Satellites and other spacecraft rely on solar arrays for power. However, solar arrays are prone to a number of defects which are necessary to detect and repair. For example, bond line defects such as voids in the adhesive may be created during assembly, and disbonds may be caused by a mismatch in the coefficients of thermal expansion and cyclic thermal conditions in space. Lamb waves are a type of ultrasonic guided wave that have been used for nondestructive defect detection in laminates. In this work, they are used to evaluate disbonds at the adhesive interface. One feature of these waves that can be used for detecting and characterizing defects is the peak amplitude. However, material damping may have a significant effect on the amplitude of propagating Lamb waves, especially in polymeric materials. In this study, a single solar cell is modeled using a thermoplastic polymer for the substrate of the array, polycrystalline silicon for the solar cell, and a silicone-based adhesive. The material properties of the substrate, including material damping, are experimentally determined. Then transient structural finite element analyses are conducted on a 2 D model of a solar cell laminate with and without material damping effects included. The results of this study are intended to inform future in-space experiment design, but can also be applied to other layered media containing polymeric materials.
Structural Health Monitoring Systems for Inflatable Softgoods Space Structures
Habitats are an essential component of any human spaceflight mission, called upon to provide the maximum amount of livable volume with the minimum amount of mass. Inflatable, deployable structures in particular have been investigated by NASA since the early 1950s and used in a number of spaceflight applications. Inflatable habitats, airlocks, and space stations can be used for in-space living quarters and surfaceexploration missions. Inflatable structures are being pursued as candidates for long-term habitats in space and on the surfaces of the Moon and Mars. Many concepts by NASA and industry utilize high-strength, low-weight softgoods materials, such as Vectran, as the primary load-bearing structure in inflatable habitats. The ability to monitor and assess the structural health and integrity of an inflatable module is an important factor in determining the feasibility of using these technologies for space habitats, especially in the presence of micrometeoroid and orbital debris (MMOD) threats. There is therefore a critical need for Structural Health Monitoring (SHM) methods to perform detection, localization, and quantification of damage to structural layers throughout the mission. This capability must be accomplished within real constraints for sensor volume, mass, and crew resources, including being able to perform effective damage monitoring of the inflatable habitat layers from the interior during a mission, either on a routine or quick-response basis. This paper discusses the design of new SHM sensor modules (consisting of colocated piezoelectric transducers and strain-gauge sensors) for inflatable softgoods and integration of a complete SHM system into inflatable habitat test articles for laboratory proof-of-concept testing. An experimental test setup for performing the initial feasibility demonstration of impact detection using the SHM sensor modules is discussed. Testing for high strain and impact detection was also conducted. The project demonstrates the feasibility of the newly-developed SHM sensor modules to provide impact detection and strain/load monitoring on inflatable softgoods for next-generation space habitation structures.
SHM in Space: On-Orbit Impact Monitoring for Satellite Structures
The rapid proliferation of satellites, scientific equipment, and debris orbiting the Earth necessitates advanced solutions to enhance the safety and efficiency of future space missions. As low Earth orbit becomes increasingly congested, the risk of unforeseen impacts grows, threatening the stability of orbital assets and jeopardizing mission success. Hence, systems to monitor the condition and integrity of satellites and other space vehicles are critical. An integrated Structural Health Monitoring (SHM) system can provide both real-time impact detection and ongoing damage assessment for orbiting vehicles, making it a key element of On-orbit Servicing, Assembly, and Manufacturing (OSAM). In this work, the authors explore the application of Acellent’s SHM system for impact detection and quantification. The system incorporates both “passive” and “active” operational modes. The first set of experiments focused on low-velocity impacts mimicking docking operations. A drop- tower test using aluminum- and composite-tipped projectiles simulated interactions between dissimilar vehicles. In the second set of experiments, each structure was impacted with projectiles at ballistic velocities of 1000–2000 km/s to simulate collisions with meteorites and other space debris.
Electromechanical Impedance Spectral Features for Structural Health Monitoring of Satellite Structures
Structural Health Monitoring (SHM) is a key enabling technology for future smart spacecraft, providing critical capabilities for in-flight structural assessment, mission decision-making, and cost-effective vehicle management. Among various SHM methods, the electro-mechanical impedance approach stands out due to its compatibility with compact, lightweight hardware and potential for onboard processing. This study exemplifies the application of a low-power electro-mechanical impedance measurement device to SHM of bolted joints in the sattelite structure. The SHM implementation incorporates Solstar’s Deke Space Communicator device. Impedance spectra were analyzed using statistical descriptors, global spectral methods (e.g., RMSD, MAPD, correlation coefficient), and alternative approaches such as band integration. The study provides recommendations on selecting damagesensitive spectral features and supports the implementation of onboard SHM systems for space applications. These findings contribute toward enabling autonomous structural monitoring and condition reporting for space assets.
Destructive Testing of Dam Spillway Gates for Development and Validation of Structural Health Monitoring Methods
Dams are used throughout the world to enable commercial traffic on rivers, provide hydropower and/or flood protection, as well as collect and store water for drinking and irrigation needs. Many dams have steel spillway gates that are used to release water or otherwise regulate flow through the dam. Failure of dam spillway gates can lead to uncontrolled release of water with significant risk to life safety and the economy. Structural Health Monitoring (SHM) of dam spillway gates is increasingly used to monitor these critical assets and provide information to enable proactive maintenance and prevention of catastrophic failures. Like most civil infrastructure, a key challenge to development of SHM systems is the acquisition of validation and/or training data of the structure in the damaged state. At Lock and Dam 22 on the Mississippi River, the spillway gates of the dam are being replaced, with the intention to scrap and recycle the old gates. This gate replacement provides a unique opportunity to perform destructive testing on the existing gates to deliberately induce damage and/or undesirable operational characteristics on the gates that would be valuable detection targets for an SHM system. This paper discusses the first sets of testing performed on the existing gates prior to their removal, where a sensor system was installed. Then, just prior to removal of the gate, a number of simulated cracks were flame-cut into. Additionally, extreme friction in the gate’s pivot points was induced using a novel procedure, and skewed lifting of the gate was induced with the use of a crane. In each case, the gate was operated, and data was collected. This paper presents the preliminary analysis and conclusions of the data collected including simple methods for detection of trunnion friction and a surprising method of using accelerometers on structural components to measure and monitor for uneven hoisting of the gate.
Single-Channel Neyman-Pearson Detection of Impulses in Tainter Valve Machinery Systems
Unexpected mechanical failures in lock fill/empty Tainter valves can significantly disrupt waterway traffic. Accurate diagnosis and prognosis of fault mechanisms are essential for timely interventions by operations and maintenance teams. To support this, an in-service lock operated by the U.S. Army Corps of Engineers was instrumented with a comprehensive sensor suite—including accelerometers, strain gauges, inclinometers, thermocouples, and electrical current transformers—spanning the entire drive system from the motor to the final gear shaft. Data were collected during valve opening and closing operations using an event-driven approach. Notably, impulses were observed in the accelerometer signal at the sector gear during valve opening, coinciding with anomalies in the sector gear inclinometer data. To detect these impulses, a single- channel binary hypothesis testing framework based on the Neyman-Pearson theorem was implemented, modeling the signal as either zero-mean Gaussian noise or a non-zero mean Gaussian distribution with deterministic impulse magnitude incorporated. To address signal non-stationarity, an optimal sliding window size was selected to maintain stationarity and statistical reliability. Results from the Neyman-Pearson detection model are discussed, offering valuable insights into the operational health of the Tainter valve system and supporting the development of more robust diagnostic tools for lock infrastructure.
Semi-Supervised Learning Approach for Image-Based Corrosion Quantification on Water Resources Infrastructure
Water resources infrastructure, such as locks and dams, are critical to the U.S. economy and public safety. Proper maintenance of these structures is essential to ensure their long-term functionality. Severe corrosion in such infrastructure can lead to significant section loss and, possibly, structural failure. Early detection and quantification of corrosion is therefore crucial to maintaining structural integrity and minimizing maintenance costs. This study explores the application of deep learning techniques, such as convolutional neural networks (CNNs), to segment corrosion areas in high-resolution images. A semi-supervised learning (SSL) approach, which leverages both labeled and unlabeled data, is employed. This method reduces reliance on fully labeled datasets, which are tedious to create and often require subject matter expertise. The proposed SSL approach is compared against baseline CNN models (DeepLabv3+, PSPNet, and UNet++), demonstrating promising results across key evaluation metrics, including mean precision, mean recall, mean F1 score, and mean Intersection-over- Union (IoU). These findings highlight the potential of SSL for rapid corrosion quantification, facilitating diagnosis, prognosis, and improved structural health monitoring of water resources infrastructure.
A Privacy-Preserving Framework Using Federated Learning for Structural Health Monitoring with Miter Gates Application
Structural Health Monitoring (SHM) systems play a critical role in maintaining the safety and operational efficiency of infrastructure such as miter gates managed by the United States Army Corps of Engineers (USACE). These gates, integral to navigation and flood control systems, demand robust and efficient monitoring techniques to predict failures and thus optimize maintenance schedules. Any unexpected shutdown costs nearly three million dollars per day to the US economy. The existing traditional data centralized machine learning approaches for SHM often face challenges, including data privacy concerns, high communication costs, and computational limitations. This study mainly explores the application of Federated Learning (FL) techniques to SHM systems using three finite element miter gate models for the loss-of-contact damage detection problem. This approach addresses these challenges by enabling decentralized training of machine learning models across multiple assets. FL ensures data privacy by keeping sensitive information local while leveraging shared global models through aggregation methods. Our results demonstrate that FL can achieve comparable prediction accuracy to centralized methods while maintaining data privacy and reducing communication overhead. This framework improves model accuracy by incorporating diverse data distributions from different gates and their operational conditions. The study also highlights the scalability of FL in handling large-scale SHM systems, making it a viable solution for USACE to extend the lifespan of their critical assets.
Generalized Damage Diagnostics of Two Miter Gates Through Domain Adaptation
Scarce monitoring data spanning unique civil infrastructure classes limits damage diagnostic generalization using data-driven approaches. While physics-based methods can address some limitations, they struggle to accurately simulate complex damage mechanisms, such as corrosion and fatigue cracks. To alleviate the data scarcity challenge, this study proposes generalizing diagnostic algorithms from structures with abundant data on condition and performance (source) to similar structures with little or not data (target). By leveraging diagnostic mappings through domain adaptation, it becomes possible to diagnose analogous structures within a class effectively. This research focuses on multidamage diagnostics between two miter gates using an adversarial domain adaptation approach. Two types of damage are simulated using validated finite element models. Diagnostic knowledge from the source gate is transferred to the target gate, enabling accurate diagnostics despite the lack of labeled data for the latter. The results demonstrate that the proposed approach achieves reasonable accuracy in diagnosing unlabeled target structures, highlighting the feasibility of transferring diagnostic information across domains with similar label ranges.
From Saltwater to Freshwater: Leveraging Uncrewed Vehicles Developed for Offshore Energy Use for Inland Waterway Structural Health Monitoring
Inland waterway infrastructure for drinking water supply, flood control, and navigation safety in the United States is aging and in need of inspection and monitoring to support continuous operations. Current in-situ methods for structural health monitoring are resource-intensive and may carry unacceptable safety risks, particularly with respect to putting human divers or vessel operators into harm’s way, while small inspection-class remotely operated vehicles may be limited in range, payload capacity, or available topside-supplied power. These traditional approaches can limit the overall efficiency, effectiveness, and ability to carry out critical monitoring when needed. Uncrewed Surface Vehicles (USVs) equipped with underwater Electric Remotely Operated Vehicles (eROVs) can offer a safe, efficient, resource-effective inspection of inland waterway infrastructure for challenging infrastructure inspection projects. Developed to conduct subsea asset inspections for offshore energy projects, these combined USV-eROV systems are capable of withstanding harsh environmental conditions with zero access to external power or fuel supplies for up to 14 days. Uncrewed vehicle-based monitoring technologies provide insights to asset owners in challenging locations, offering early context of developing threats to inland waterway structures. Despite these technological advances, conducting inspections with uncrewed systems requires substantial planning and effort well beyond simple selection of payload instruments. In this paper, we will present key lessons learned of USV-eROV asset inspections in the offshore environment and discuss how these methods are relevant to the inland waterway asset management space.
Pitting Corrosion Model Updating Using Experimental Data
Both physics-based (deterministic) and statistical models are widely used in structural health monitoring (SHM) research due to their ability to predict damage progression, particularly in scenarios with limited data availability. However, while all parameters in statistical models and certain parameters in physics-based models cannot be directly measured, updating these parameters using measurement data is essential for ensuring accuracy. This study presents a novel approach to updating a hybrid corrosion model using accelerated corrosion experimental data combined with an amortized likelihood-free Bayesian inference method. In the proposed framework, pit numbers and dimensions developed after different corrosion time durations are first measured using a high-precision laser scanner. Two reaction coefficients and two statistical parameters, which are not directly measurable, are selected for updating in the hybrid simulation. A summary network and a conditional invertible inference network are jointly trained on extensive simulation data to learn the mapping between observed data and model parameters. The trained network is then utilized to infer the posterior distribution of the model parameters based on experimental measurements. The results demonstrate that the proposed conditional invertible neural network-based inference method can effectively and efficiently update the hybrid model, enhancing parameter estimation for complex damage mechanisms such as corrosion. Additionally, a key advantage of this approach is its computational efficiency, as it can perform inference within seconds—significantly faster than other likelihood-free inference methods. This capability makes it particularly suitable for real-time SHM applications, improving both accuracy and practicality in damage assessment.
Experimental Characterization and Computer Vision-Based Detection of Pitting Corrosion on Stainless Steel
Pitting corrosion is a form of corrosive damage that leaves cavities in metallic materials, which can significantly compromise structural safety and reliability. For instance, stainless steel, as a versatile structural material, can subject to pitting corrosion, regardless of its high corrosion resistance. The pit growth typically exhibits an initial high growth rate during nucleation, followed by an eventual saturation limit, which will ultimately lead to material failure. Although pitting corrosion is a prevalent damage mode, there is limited data on pit development under different corrosive conditions. Also, it remains challenging to efficiently detect pitting corrosion on large-scale water resources infrastructure. Therefore, this study aims to not only experimentally characterize the evolution of pit morphologies (i.e., depth and surface opening area), but also adopt computer vision techniques to detect pitting corrosion. In particular, an accelerated corrosion experiment was designed and implemented to introduce pitting corrosion to stainless steel in a controlled manner. The three-dimensional pit morphologies were measured using a high-precision laser scanner. Different statistical models were implemented to characterize the evolution of pit morphologies. Furthermore, this study also trained and compared several different computer vision algorithms to identify a promising structural health monitoring approach to detect pitting corrosion in a scalable and non-contact manner.
Bayesian Neural Networks vs. Model Updating for Inference of Miter Gate Damage
Miter gates are critical components of inland waterways and their failure can have significant consequences. These gates must fully contact a wall or stress concentrations develop, leading to cracking and failure. To detect gaps in the contact, we compare two inference methods: Bayesian model updating using the transitional Markov chain Monte Carlo (TMCMC) method and neural network-based Bayesian inference. TMCMC is a widely used method, but inference is computationally expensive. In contrast, neural network-based Bayesian inference trains a neural network upfront and infers damage parameters using the computationally efficient neural network, dramatically reducing runtime as more datasets are gathered and parameters are updated. Through a case study data gathered at a specific miter gate, both methods are compared on their performance inferring boundary condition degradation using vision- based displacement data. Results show that neural network-based Bayesian inference achieves similar accuracy to TMCMC updating with a significant reduction in computational costs from hours to seconds. These computational advantages may aid in online and edge applications at miter gates and other civil structures. With regular updates of miter gate bearing contact damage, decision-makers can more effectively maintain miter gates.
Assessing the Role of Quoin Gaps on Miter Gates Damage States Through Optimization
Miter gates are cyclically loaded structures present along waterways that allow for ships and cargo to traverse significant changes in elevation. The miter gate leaves are mounted on either end of a lock chamber from a gudgeon anchorage system, typically in pairs, and function by being swung into place about a pintle assembly by mechanical operating machinery. Along either side of a miter gate leaf run the quoin and miter contact blocks. When gates are placed into service or undergo maintenance, engineers use procedures to set tolerance gaps between the quoin contact blocks and the lock wall. If the gaps are set too tight, then when the gates are brought to miter, premature contact can cause prying action between the gudgeon and wall. This causes significant increases in stress in the gudgeon anchorage, a failure critical member (FCM), and has been observed during several structural health monitoring (SHM) initiatives on in-service infrastructure. Alternatively, if the gap tolerance is not set tight enough, when hydrostatic load is applied to horizontally framed miter gates, the transfer of load causes the gate to thrust towards the wall until the quoin contact blocks bear against the lock wall. When contact is delayed or does not occur, the load is transferred to the pintle and gudgeon, which resist this thrusting, rather than the lock wall. This significantly raises the stresses in these regions and leads to damage. SHM techniques have been previously developed to detect the presence of quoin gaps using both contact sensors and visionbased methods in an effort to inform maintenance decisions. The specifics of gap sizes and locations can be confirmed under gravity load using instruments known as feeler gages. There is currently no standardized procedure for setting these quoin gaps; therefore, there is a need for a rigorous analysis to standardize gap lengths that assesses tolerance gap sizes and their consequences. In this study, a suite of experiments is run using finite element analysis (FEA) on a series of numerical models. Focus is placed on horizontally framed miter gates around the typical size of 60 ft tall. Within the numerical experiments, optimization techniques are employed to find the optimal quoin gaps sizes that results in the minimization of stresses in the gudgeon and pintle. Validation of this optimization will occur though the physical testing of a miter gate fully instrumented with an SHM system. The experimental test set up that is currently under development will also be discussed.
Value of Information Analysis for Structural Health Monitoring Considering Multiple Damage Modes
The Value of Information (VoI) framework is a crucial component in evaluating, designing, and optimizing the Structural Health Monitoring (SHM) system by assessing its overall benefit in lifecycle asset management. It focuses on conducting a cost-benefit analysis of decision-making throughout the lifecycle. In practice, the overall damage in a structure consists of multiple damage modes that collectively determine its health. Therefore, it is essential to develop a VoI framework that can incorporate multiple damages and their evolution—first, to evaluate the performance of an SHM system, and second, to optimally design the SHM system. A multi-damage-mode VoI framework is proposed, and its application to the lifecycle assessment of a miter gate structure is discussed.
Leveraging Machine Learning for Predictive Maintenance of Lock and Dam Infrastructure Using Real-World Data
Structural health monitoring (SHM) of water resource infrastructure, such as locks and dams, requires innovative approaches due to their complex environments, accessibility challenges, and variable loading conditions. Machine learning (ML) plays a significant role in detecting anomalies and enabling predictive maintenance capabilities. ML provides a data-driven approach to identify potential structural issues before failure occurs. This study enhances an ensemble-based anomaly detection framework by integrating advanced feature extraction techniques and sensor fusion methods to improve the monitoring and assessment of lock and dam health. A timeseries segmentation approach is utilized to divide sensor data into discrete periods, which are then characterized by a set of statistical features. These features serve as inputs to a Principal Component Analysis (PCA), allowing for effective dimensionality reduction. Advanced feature-level fusion is achieved by leveraging multi-channel strain sensor data, leading to improved anomaly detection and system reliability. This technique enables the integration of data from multiple sensors into a unified framework, offering a comprehensive assessment of structural conditions. An ensemble of ML-based anomaly detection methods is applied to recognize deviations in sensor data patterns, providing an early warning system for potential structural concerns. To refine classification accuracy, the anomaly detection system is supplemented with existing models. This hybrid strategy enhances the interpretation of detected anomalies, reducing false positives and improving the reliability of SHM systems. By merging data-driven ML techniques with well-established physics models, the proposed approach offers a more robust method for predictive maintenance in lock and dam infrastructure. The framework is validated using real-world strain sensor data from a lock gate, where an existing crack is a primary concern for the maintenance team. This study presents a flexible and scalable predictive maintenance framework tailored for lock and dam infrastructure. The proposed approach emphasizes the efficient deployment of SHM methods in real-world scenarios, addressing a critical challenge in the field by providing a practical pathway for implementing SHM solutions effectively.
Augmented Reality Visualization of Wearable Strain Sensor Data for Monitoring Low Back Activation During Weightlifting
Resistance training is a widely practiced method for building strength, endurance, and muscle definition across various muscle groups. Certain weightlifting exercises are designed to isolate specific muscles. However, unintentional lower back engagement during these movements can reduce the effectiveness of the exercise and increase the risk of muscle strains that can lead to injury. Addressing incorrect form in weightlifting has been accomplished by a variety of methods, including wearable sensors, but there exists a need for a real-time, noninvasive solution to correct weightlifting form on-thefly. To address these challenges, an augmented reality (AR) interface was developed to directly provide form feedback to the weightlifter based on wearable sensor skin-strain measurements from Motion Tape (MT), which is a self-adhesive skin-strain sensor formed by drop-coating piezoresistive carbon nanomaterial solutions directly onto kinesiology tape. Participants performed sets of 10 repetitions of biceps curls (where low back activation is undesirable), with and without the aid of AR feedback. During testing, each participant wore a pair of MT sensors on their lower back at the left and right sides of the pelvis. These MT low-back strains were displayed in real-time during exercise in the Microsoft HoloLens 2 headset. Analysis of the test data showed that MT recorded lower levels of low-back engagement across three sets of each exercise when the subject was aware of the strains visualized in AR.
A Solitary Wave-Based Tonometer for Intraocular Pressure Measurement
Glaucoma is an age-related incurable disease and the second cause of blindness in the world. The risk of developing glaucoma increases in the presence of elevated intraocular pressure (IOP), a risk factor that can be modified through invasive surgery. In clinical practice, IOP remains the cornerstone of the diagnosis and management of glaucoma, and Goldmann applanation tonometry (GAT) is considered the gold standard in IOP measurements. However, GAT, as any other commercial system, provides a single data point value that is unable to frame the natural circadian rhythm and sporadic surges of IOP. This capability is only possible with a portable device that patients can eventually self-administer anytime anywhere. To serve the purpose, the device must be easy-to-use and not requiring sterilization or topical anesthesia. Additional desirable features are low price and adaptability to patient’s eyeball geometry. To address these needs, our group has conceptualized, assembled, and tested a new tonometer based on highly nonlinear solitary waves propagating along a chain of particles, the last of which is in dry contact with the eye to be evaluated. The hypothesis is that the time of flight (ToF) of the waves within the chain is monotonically associated with the IOP. In this study, the effect of central corneal thickness (CCT) and IOP of artificial corneas made of polydimethylsiloxane was quantified experimentally and modeled numerically using static and dynamic finite element analyses. The numerical and experimental results agreed in identifying a correlation between ToF and IOP and CCT. However, some quantitative discrepancies between numerical and experimental results warrant the improvement of the model.
Respiratory and Rehabilitation Exercise Measurements Using Nanocomposite Wearable Sensors
Diastasis recti abdominis (DRA), a common postpartum condition marked by the separation of the rectus abdominis muscles, can impair core stability, respiratory efficiency, and pelvic floor function. Despite the demonstrated clinical benefit of targeted exercise interventions, there is an urgent need for wearable technologies that could precisely assess rehabilitation progress and outcomes. This work presents a soft, low-profile, reusable, wearable sensor designed for high-resolution, real-time monitoring of abdominal mechanics during core-focused exercises. The skin-strain sensors were fabricated by sandwiching a multi-walled carbon nanotube thin film within two elastomer layers. Female participants with no history of childbirth were recruited to perform clinically relevant tasks, such as diaphragmatic breathing and various exercises. Wearable sensor skin-strain data, surface electromyography, and commercial respiration belt measurements were recorded for comparison. The results confirmed the system’s ability to resolve task-specific variations in skin-strain while providing insights into muscle coordination and respiratory performance. The vision is to leverage these unique sensing streams to provide patients with real-time feedback while deriving data-driven, personalized rehabilitation strategies.
Non-Intrusive Cognitive Load Estimation Using Footstep-Induced Structural Vibration Signals
Cognitive load estimation is crucial for understanding human performance during demanding tasks. Elevated cognitive load can impair decision-making and increase the risk of fatigue or burnout. Consequently, real-time monitoring of cognitive load is essential in high-stakes environments such as healthcare, where even subtle changes in human movement can have significant consequences. Conventional methods for cognitive load assessment predominantly rely on neuro-imaging, physiological signals, or subjective reporting, which are often not real-time, intrusive and can cause user discomfort. In this study, we propose the use of structural vibrations as a non-intrusive and privacy-preserving modality for cognitive load estimation, offering a promising alternative in healthcare and other sensitive domains. We conducted controlled experiments with eight participants under two walking conditions: a baseline of normal walking and a dual-task condition where participants walked while engaging in cognitively demanding interactions with a conversational agent. Comparative analysis revealed distinct changes in gait dynamics and footstep-induced structural vibration patterns under cognitive load. Our experimental results identified clear sensor signatures corresponding to varying levels of cognitive demand, demonstrating the potential of structural vibration metrics as indicators of cognitive load. We achieved 68.00% accuracy in cognitive load classification using structural vibration signals. Furthermore, for person identification, we attained an accuracy of 82.00% based on structural vibration data.
Smartphone-Based Dynamic Testing of a Rehabilitated Pedestrian Bridge: A Comparative Study with Traditional Accelerometers
The structural health monitoring (SHM) of bridges plays a vital role in ensuring the safety and longevity of aging infrastructure. Recent advances in sensor and mobile technologies have enabled low-cost, scalable, and portable alternatives to conventional monitoring systems. This study presents the development and validation of a cloudintegrated methodology for dynamic testing using smartphones, with a focus on modal identification of bridge structures subjected to ambient vibrations. A rehabilitated pedestrian bridge in Zagreb, Croatia served as the case study. Operational modal analysis (OMA) was performed using both conventional piezoelectric accelerometers and embedded MEMS accelerometers within smartphones. Data were collected in parallel and processed using Artemis Modal Pro software. The results demonstrated a strong correlation between the two measurement systems, confirming the capability of smartphone-based sensing to accurately capture key dynamic characteristics. The proposed approach offers a viable and accessible alternative for SHM, reducing cost and logistical complexity while maintaining sufficient precision for practical engineering applications. Moreover, the integration of mobile technology with automated data processing workflows opens new possibilities for rapid deployment and broader implementation in infrastructure diagnostics. This work contributes to the growing field of smart monitoring solutions, emphasizing the practicality of smartphone-based systems in both research and real-world engineering contexts.
Localization of Delaminations in Composite Laminates by Nonlinear Interface Stresses
While often barely visible, delaminations in composite laminates can critically degrade structural performance and safety. However, localizing small delaminations that minimally alter local stiffnesses remains challenging. On the other hand, contactinduced nonlinearity arises when a delamination periodically closes and opens like breathing, resulting in time-varying normal and shear stresses across the delamination interfaces. This study defines these stresses as nonlinear interface stresses (NISs), emphasizing their application to the localization of delaminations in composite laminates. The NISs exclusively appear within “breathing” delaminations, acting as multi-tone harmonic excitation sources that produce higher harmonics. Additionally, the NISs can serve as ideal indicators for localizing delaminations due to their inherently localized nature. The NISs are reconstructed from nonlinear steady-state wavefields (NSWs) of composite laminates at higher harmonics. The approach based on NISs is experimentally validated on a glass fiber-reinforced polymer laminated plate containing multiple local delaminations fabricated using Teflon sheets, low-speed impacts, and local heating. A piezoelectric actuator, mounted at the center of the plate, generates single-tone harmonic excitations, while a scanning laser Doppler vibrometer measures the NSW of the plate. The experimental results reveal that not only large thermal delaminations but also small delaminations caused by Teflon inserts and low-speed impacts can be spatially characterized with locations and sizes.
Approximate Analytical Solution for a Simply Supported Beam on Tensionless Winkler Foundation Under Multiple Concentrated Loads
This work presents an approximate analytical solution for simply supported beams resting on a tensionless Winkler foundation and subjected to multiple point loads. The foundation is modeled as a series of discrete linear independent springs and gaps may exist between the beam and the springs. The two-stage technique approximates the response of the tensionless foundation: in the first stage, the technique identifies the contact and noncontact regions based on an iterative procedure, while in the second stage, the technique redistributes the springs in the contact region only. Results are validated and compared with a benchmark solution. The numerical examples confirm the efficiency and accuracy of the proposed method.
Compound Defect Diagnostics of Swind Turbine Gearboxes Via Variational Mode Decomposition, Hjorth Parameters and Machine Learning
This study aims to develop a robust and automated diagnostic framework for identifying and classifying compound defects in WT gearboxes. The proposed methodology adopts a multi-phase approach. Vibration signals corresponding to various compound defect scenarios across multiple gearbox stages are initially collected from a WT gearbox system. From these signals, Hjorth parameters (activity, mobility, complexity), representing time-domain features, are computed for all gearbox health conditions. Simultaneously, the signals undergo variational mode decomposition (VMD) processing to extract intrinsic mode functions (IMFs). A comprehensive set of statistical features encapsulating time-frequency domain characteristics is subsequently derived from these IMFs. Data augmentation is employed to enhance the robustness and discriminatory power of the dataset, leading to the formulation of an enriched feature set by integrating the Hjorth parameters (time-domain features) with the statistical features (time-frequency domain features). The feature set is subjected to machine-learning-based multi-class classification to diagnose compound gearbox faults. The study systematically evaluates the diagnostic performance of the augmented feature dataset and benchmarks it against individual feature sets. The findings underscore the effectiveness of the proposed methodology in accurately diagnosing compound faults with reduced dependency on complex signal post-processing techniques, thereby advancing the state of fault diagnostics in WT gearboxes.
Multicopter Motor Damage Diagnosis Via Functionally Pooled Time Series Models: Experimental Assessment Via a Series of Flight Tests
This study presents a unified statistical framework for detecting, identifying, and quantifying damage on multicopter propellers through functionally pooled (FP) time series models. Unlike conventional autoregressive models, FP models feature parameters that are explicit functions of damage magnitude, enabling parsimonious representation of damage dynamics evolution while accounting for cross-correlation between different damage states. The methodology integrates three capabilities within a single framework: health detection, motor identification, and precise damage quantification with statistical confidence intervals. Experimental validation is conducted using a hexacopter executing figure-eight flight patterns under turbulent conditions, with blade damage ranging from 2 mm to 10 mm investigated across three different rotating motors. The proposed approach achieves effective damage detection and accurate motor identification using short data segments (4 seconds) from single-channel inertial measurement unit (IMU) measurements during flight. Damage quantification is demonstrated with corresponding confidence intervals, providing statistical reliability assessment. The framework’s effectiveness is evaluated through analysis of 160 s of independent test signals, demonstrating its potential for real-time fault diagnosis in autonomous aerial vehicles.