Structural Health Monitoring Research Articles (Page 1)

Phased array technology involves the coordinated control of multiple elements to steer and focus elastic, electromagnetic, light, seismic, and radio waves in a specific location or direction. In structural integrity applications, it enables the precise inspection of materials and the identification of flaws/defects in structures. In this paper, we proposes a novel phased array method based on the steering and focusing of thermal waves, not previously explored for applications in NDT, named Phased-Array Thermography (PAT). This new three-dimensional approach aims to overcome the main limitations of most of the Active Infrared Thermography (IRT) methods that uniformly heat the component surface and generate a normal temperature gradient, resulting in lack of control in the gradient direction and, ultimately, limiting the identification capabilities of IRT. PAT leverages an array of heating elements to precisely steer and control the thermal wavefront. A closed-form analytical solution of the thermal wave propagation is derived and validated against numerical simulations. Then, the accuracy of proposed method is assessed via thermal Finite Element (FE) simulations of an aluminium plate by comparing PAT with a commonly used IRT technique such as the Pulsed Thermography (PT). Finally, experimental analyses of an aluminium plate with flat bottom holes and a composite plate with impact damage are performed to validate the proposed methodology. This novel approach to thermal wavefront steering via phased array technology introduces a previously unexplored mechanism for controlled heat wavefront, with transformative potential for non-destructive evaluation, structural health monitoring, and adaptive manufacturing systems.

ABSTRACT This study investigates the propagation characteristics of Rayleigh waves in a layered medium consisting of a nonlocal fiber‐reinforced elastic layer over a nonlocal elastic substrate, incorporating surface corrugation and imperfect interface conditions. Utilizing a harmonic wave analysis combined with the variable separable technique, analytical expressions for displacement fields in both media are derived. Numerical simulations are performed using Mathematica software, where the dispersion relation is derived through the application of appropriate boundary conditions solved and graphically represented. These simulations elucidate the influence of nonlocal elasticity parameters, fiber orientation, interface stiffness, corrugation amplitude, and layer thickness on the wave propagation characteristics. The findings demonstrate that nonlocal elasticity and interfacial imperfections markedly influence wave behavior, causing significant reductions in phase velocity and inducing intricate dispersion phenomena. This novel framework, which integrates size‐dependent elasticity with complex interface effects, provides critical insights for applications in nondestructive evaluation, structural health monitoring, and the development of advanced wave‐based sensing and vibration mitigation technologies.

Leveraging vibration sensor data and machine learning for effective structural health monitoring of the KW51 bridge

Abstract In this study, a data-efficient and interpretable framework is presented for anomaly detection in bridge structural health monitoring (SHM) systems. Addressing the limitations of data-hungry machine learning (ML) models, the proposed method enables near-real-time detection using efficient data. The framework extracts autoregressive coefficients (AR-Coeffs) from acceleration responses along with tactically employing the logarithm of the signal standard deviation (Log-Std) as a complementary feature. It employs a Weighted K-Nearest Neighbors (WKNN) classifier to rapidly and accurately detect anomalous behavior. Additionally, the method can localize faulty sensors, enhancing actionable insights for maintenance decisions. Its robustness was validated using acceleration data from two real-world cable-stayed bridges: Sutong Bridge (IPC-2020 dataset) and Yonghe Bridge (SMC dataset). While the latter is used for the first time as an anomaly detection benchmark, results from the former demonstrate high classification accuracy (up to 98.7%) and superior computational efficiency, outperforming previous studies by a factor of 4 to 8 in runtime, confirming the method’s effectiveness under varying operational conditions. Last but not least, the study demonstrates how fluctuations in ambient temperature, both increases and decreases, affect sensor outputs, leading to various anomalies in the data, an often-overlooked aspect in the SHM literature. By emphasizing diversity in training samples rather than relying solely on sheer quantity, the proposed methodology offers new insights into the applicability of ML in SHM, achieving high accuracy at a low cost.

Automation in crack and bug-hole detection through non-destructive evaluation methods remains a major challenge in Structural Health Monitoring. Typically, machine learning models are fed with original, grayscale, or binary formats of concrete images. Original images retain excessive color data, grayscale formats reduce color without preserving structural relevance, and binary images often lack sufficient detail for assessing damage type and severity. To overcome these limitations, multilevel colored image thresholding using meta-heuristic algorithms such as Particle Swarm Optimization, Genetic Algorithm, Jaya and Sine Cosine Algorithm—guided by Otsu’s objective function. This technique enhances the contrast, edges, and texture boundaries, which serve as crucial primitives for object recognition, compared to original images. Additionally, it increases pixel connectivity, thereby simplifying image analysis for deep learning and machine learning applications. In fact, this method reduces the number of colors by an average of 97.3%, significantly decreasing computational load, while maintaining a high Structural Similarity Index (SSIM) of 0.873. Experimental results demonstrate that the optimized images outperform the original, grayscale, and binary formats in both object detection performance and precise evaluation of damage severity and progression. This fusion of optimization and perceptual image representation presents a promising advancement for automated structural damage assessment.

Bridge collapses are catastrophic infrastructure failures, leading to loss of life, disruption of mobility, and erosion of public trust. This paper analyses four major Indian bridge collapses (2013–2025) — Ultadanga, Talkies, Taratala (Majerhat), and Gambhira — using a Case–Cause–Lesson framework. A comparative study of Majerhat (2018) and Gambhira (2025) is presented to highlight recurring weaknesses in design, construction, maintenance, governance and have revealed severe deficiencies in structural health monitoring, inspection protocols, and disaster preparedness. This paper integrates case-based forensic analysis with international benchmarking to identify systemic gaps in bridge safety management. Using a mixed qualitative–quantitative framework, the study examines causes, consequences, and institutional responses to recent bridge failures. A comparative overview of international best practices (Japan, UK, USA) informs a roadmap for implementing IoT-based Bridge Health Monitoring (BHM) and Digital Twin frameworks in Indian conditions. Results highlight the urgent need for integrated governance, transparent maintenance systems, and community-focused disaster management protocols.

The high cost of traditional structural health monitoring systems limits their application to only a few major bridges, leaving most structures unmonitored between manual inspections. To address this issue, this study proposes a UAV mobile detection device (UMD) system that integrates a Raspberry Pi, data acquisition module, and accelerometer for rapid, contact-based vibration measurement. A vibration transmission model between the UMD and the bridge deck is developed to guide hardware design and quantify the influence of isolator stiffness and damping. The UMD’s performance is validated through both laboratory floor tests and field bridge experiments, demonstrating reliable identification of modal frequencies in the range of 0.00–51.95 Hz with a maximum acceleration error below 0.01 g and a relative modal frequency deviation within 3.4%. The analysis further determines that an accelerometer resolution of 0.02×10−1 g is required for accurate frequency domain measurement. These findings establish the UMD as a fast, low-cost, and accurate tool for rapid bridge vibration assessment and lay the groundwork for future multi-UAV synchronized monitoring.

ABSTRACT The absence of excitation information and the presence of measurement noise pose significant challenges to accurate damage detection. To address these issues, this study introduces a robust output-only damage identification framework that integrates an enhanced response reconstruction scheme with a collaborative swarm optimisation algorithm (CSOA). In the proposed framework, the available structural responses are partitioned into two subsets. The second set measurements are reconstructed from the first set using the enhanced response reconstruction technique with an improved regularised method. The damage identification task is then formulated as an optimisation-based inverse problem, where the objective function is defined by the discrepancy between the measured and reconstructed responses. Subsequently, CSOA is employed as the search tool to identify local damages by minimising this objective function. The proposed approach is validated through numerical studies on a simply supported truss, a cantilever beam and the Guangzhou New TV Tower, as well as experimental verification on an eight-story steel frame. The effects of measurement noise and damage scenarios are systematically investigated. Results demonstrate that the method can reliably and efficiently identify both the location and severity of single and multiple damages, even with noisy output-only data.

Wind turbines, despite being a sustainable and environmentally friendly method of electricity generation, often suffer structural damage during storms or typhoons. To enhance their resilience under such conditions, this study proposes two optimization strategies: (i) palm leaf–inspired blades and (ii) multi-rotor spatial layouts. Laboratory-scale experiments were conducted on centimeter-scale 3D-printed models (approximately 1:1000 scale representation), and specimens were tested in a custom-built wind tunnel. The inflow was steady (∼23.7 m/s) and conditioned by a collimator, serving as a proxy for severe wind conditions. Inclination and vibration were monitored, and the data were analyzed using Fourier transforms, t-tests, and ANOVA. The results showed that the palm leaf–inspired design exhibited inferior performance compared to conventional blades, suggesting limited applicability in this context. The three-rotor system achieved the smallest average inclination angles (3.36 ° lateral, 3.08 ° forward), indicating lower tower-base bending demand and a greater stability margin (i.e., delayed buckling), and the lowest vibration response (0.341), indicating reduced cyclic stress amplitudes and improved fatigue resistance. These findings highlight the three-rotor system as a promising design for enhancing wind turbine resilience under storm-level wind conditions.

Structural health monitoring using strain data faces a critical challenge: decoupling subtle structural degradation signatures from the dominant influence of operational loads. This paper introduces a novel methodology to address this by synergistically combining a custom health indicator (HI) with graph neural networks (GNNs). The proposed HI, derived from the cumulative absolute first derivative of strain over time, effectively isolates load-independent features indicative of damage progression. These features serve as input to our proposed GENConv with Edge Attributes (GENEA) model, a GNN that explicitly models the spatially distributed sensors as an interconnected network, leveraging spatial interdependencies and edge attribute information within the strain field to enhance damage assessment. This integrated approach enables accurate structural stiffness reduction estimation (diagnostics) and remaining useful life (RUL) prediction (prognostics). Applied to strain data from fatigue tests on representative aeronautical composite panels, the methodology is rigorously evaluated using Leave-One-Panel-Out cross-validation. The framework shows promising performance on unseen test data, although challenges in generalizing to out-of-distribution specimens were also identified, highlighting the importance of a diverse training set for real-world applicability. Experimental results confirm the framework’s superiority. The proposed GENEA model significantly outperforms both a fundamental multi-layer perceptron and a spatially aware convolutional neural network baseline, and successfully generalizes to an unseen panel with a different sensor count. This validates the benefits of using a tailored GNN framework to learn robust, geometrically invariant patterns from load-decoupled spatial strain data.

Abstract Three-dimensional modelling of buildings requires reliable data sources and sophisticated tools capable of delivering exhaustive models that can facilitate property management. The authors devised a methodology for 3D building modelling using only open-access geospatial databases like Light detection and ranging (LiDAR) scanning, map and photogrammetry resources, and the relevant publicly available land and topography databases. The models integrate building geometry and detailed object information, which makes them versatile tools for property valuation, management, and structural health monitoring. The study brings together Building Information Modeling (BIM) and Geographic Information Systems (GIS) tools that can integrate spatial data and build precise models with details of critical parts of buildings, such as roofs, walls, window and door openings, balconies, terraces, hard infrastructure, and other structural and fit-out components. The methodology’s performance and versatility were verified on single-family residential buildings in Kraków (Poland). The results have confirmed that the constructive collaboration of open-access geospatial data, GIS, and BIM yields high-grade 3D models for structural health monitoring, action planning, and building life cycle management. This approach leads to effective property (resources) management and streamlines planning and taking actions over the life cycle.

Abstract Micro‐damage detection and recognition with sensors are crucial for structural health monitoring (SHM) in extreme environments. However, traditional high‐temperature sensors are mostly restricted below 200 °C and face poor conformability; simultaneously achieving high‐temperature resistance and good flexibility remains a major challenge. Therefore, this study presents a high‐temperature flexible piezoelectric sensor based on polyacrylonitrile (PAN)/zinc acetate (Zn(Ac) 2 )/multi‐walled carbon nanotubes (MWCNT) (PZM) composite nanofiber mat via electrospinning and thermal‐treatment processes. Weakened interaction between PAN nitrile groups reduce its cyclization temperature to 259.68 °C and improves its cyclization degree to 83.33% (260 °C). The fabricated PZM sensor demonstrates an exceptional performance across a broad work temperature range (25–550 °C), high sensitivity (1.84 V/N), and high piezoelectric output (15.78 V). The PZM sensor‐based monitoring system not only demonstrates comparable monitoring capabilities to PZT sensors but also achieves damage diagnosis in complex curved structures under extreme high‐temperature environments. The PZM high‐temperature flexible piezoelectric sensor health monitoring system can accurately measure millimeter‐level damage, and long‐term operational stability and durability (5000 press‐releasing cycles repeated tapping test without performance loss at 400 °C). These demonstrations illustrate that the PZM sensor offers a versatile solution for hetero‐components health monitoring in high‐temperature environments.

Abstract This study evaluated the accuracy of crack detection and width measurements under various scanning conditions. The data were collected through Terrestrial Laser Scanner (TLS) scanning of the panel, which was tested with different scanning distances and incident angles, as well as cracks with known tilt angles on the panel. Mixed pixels are included in the point cloud import to eliminate the loss of valid crack information. A series of controlled experiments assessed how the inclusion or removal of mixed pixels affects the measurement accuracy. Gaussian fitting and intensity-based clustering were applied during data processing to enhance the precision of crack point extraction. The caliper-measured crack widths served as the ground truth, allowing the calculation of the absolute error percentage (AEP) for TLS-derived measurements. The results indicate that mixed pixel preservation in point cloud processing reduces AEP by 15–30% compared to removal, while good scanning conditions—15°–30° incident angles, ≤4m distance, and ≤30° tilt angles—optimize AEP to ≤1.2% for 2 mm cracks and ≤0.8% for cracks >2 mm. Larger angles (>60°) and greater distances (>6m) increase the errors, particularly for cracks <4 mm. The proposed method is well-suited for laboratory testing, prefabrication quality control, and structural health monitoring tasks that require high-precision, non-contact crack measurement. It also provides practical guidance for the effective application of TLS in real-world engineering practices.

This research analyzes the implementation of terrestrial laser scanning (TLS) in Building Information Modeling for Bridges (BrIM) for the structural monitoring and analysis of the Villena Rey Bridge, considering that bridges undergo progressive deterioration due to environmental factors and dynamic loads, making advanced monitoring technologies essential. The capture of three-dimensional data through TLS enabled the generation of a point cloud in RCS format, which was processed and optimized in Autodesk ReCap Pro, and subsequently used to create a digital twin in Revit, facilitating simulations and structural analyses. Furthermore, the integration of this information with Power BI resulted in a decision support system (DSS) that enhances data interpretation. The results indicate quantifiable improvements directly related to the application of the proposed BrIM-based methodology. Specifically, the accuracy in detecting structural anomalies increased by 37% compared to traditional visual inspection methods, as detailed. Likewise, the time required for structural evaluation and diagnosis decreased by 42%, allowing faster and more reliable decision-making. Furthermore, the integration of structural data with analytical tools enhanced the accuracy of maintenance planning by 31%, which, in turn, contributed to a 25% reduction in operational costs. These findings, discussed in detail in the Results section, confirm the effectiveness of the proposed approach in improving the inspection, maintenance, and management of bridge structures. Finally, as highlighted in the Conclusions, the integration of technologies such as TLS, Revit, and Power BI represents a significant step forward in digital road infrastructure management and provides a foundation for future research focused on refining and expanding this methodology.

Conformal electronics are distinguished by their unique characteristics, such as the integration of structure and function and their conformability with complex geometries. These features unlock a broad spectrum of applications, including structural health monitoring and the creation of metasurfaces. However, the current landscape of large-scale curved electronic fabrication is characterized by a significant gap in specialized equipment and standardized strategies. In this context, we introduce a pioneering strategy that leverages robotized electrohydrodynamic (EHD) printing for the conformal fabrication of large-scale curved electronics on 3D surfaces. This comprehensive multi-robot EHD conformal printing strategy integrates several critical components, including plasma surface treatment, EHD conformal printing, and near-infrared (NIR) sintering processes. These are supported by enabling technologies such as 3D surface reconstruction and precise hybrid positioning. Notably, our strategy achieves 5 µm printing resolution via EHD lithography and 35 µm repeatable positioning accuracy. After plasma treatment, conductive patterns on FR4 substrates reach 5B-level adhesion strength. NIR sintering enables high-efficiency sintering within only 125 s. Seamless integration of these processes into multi-robot collaborative equipment enables the fabrication of large-area conformal electronics, such as 400 mm × 1000 mm unmanned aerial vehicle wings and 650 mm × 350 mm satellite shells, and supports multi-layer systems including wires, LED arrays, antennas, and sensors. This strategy possesses substantial potential to transcend the limitations inherent in traditional fabrication methods, paving the way for new frontiers in conformal electronics across a variety of applications, including smart wings and satellite surfaces.

This study investigates an autoencoder trained with short-term sequences of raw time-domain signals for unsupervised damage detection and localization under varying temperatures. The approach is designed to overcome the lack of transferability in feature-based methods and is therefore tested on both active (ultrasonic guided waves) and passive (vibrational responses) structural health monitoring systems. Both systems are highly sensitive to temperature variations, which alter structural responses and wave propagation properties without inducing permanent changes, thereby necessitating robust normalization strategies. For a cantilever beam in a climate chamber and an active piezoelectric system placed on a composite plate, the data-driven strategy successfully compensated for temperature effects, enabling sensitive damage analysis. In vibration-based monitoring, the model performed best when trained on temperature ranges rather than discrete states. For guided waves, damage was localized with consistently low error by integrating the autoencoder’s residual covariances with the Reconstruction Algorithm for Probabilistic Inspection of Damage (RAPID) algorithm. Critically, this was achieved without requiring a comprehensive intact-state data set across all temperatures. These findings demonstrate that the autoencoder framework is robustly applicable across both active and passive SHM domains, and the developed enhancements are fully transferable.

This study proposes a novel structural damage detection method that integrates Mel-frequency cepstral coefficients (MFCCs) and deep autoencoder (DAE) networks to enhance robustness against measurement noise and uncertainties. MFCCs are extracted from power spectrum ratios derived from vibration signals to serve as noise-resilient features representing the dynamic characteristics of structures. A DAE is trained using healthy-state MFCCs to learn their intrinsic patterns, and reconstruction errors on testing data are subsequently analyzed. To account for uncertainties, multiple measurements are performed, and the resulting mean absolute error (MAE) distributions are modeled using Gaussian processes. The Bhattacharyya distance is then employed to quantify the differences between MAE distributions under healthy and potentially damaged states, leading to the definition of a damage indicator. Two case studies, including laboratory-controlled experiments on simply supported beams and field testing on a steel bridge, are conducted to validate the method. The results demonstrate that the proposed approach effectively identifies structural damage and exhibits strong resilience to varying noise levels, outperforming conventional MFCC-based techniques. This method shows significant potential for practical applications in structural health monitoring under uncertain environments.

Seismic intensity rise and aging of high-rise structures have driven the necessity to incorporate maintenance planning into seismic performance evaluation. This paper presents an overview of peer-reviewed articles between 2005 and 2024 to discuss how maintenance planning can improve the safety and resilience of high-rise structures under seismic loading. A PRISMA-informed systematic review method was applied to identify 465 research papers from scholarly databases first and 82 of them as being relevant after screening. The considered works collectively prove that classical seismic assessment to a significant extent overlooks the cumulative effect of material degradation and therefore gives deceptive predictions of structure performance at future times. Incorporation of maintenance preventive and predictive into nonlinear dynamic analysis models considerably enhances seismic response estimation accuracy. Outcome of some analytical and experimental research shows that regular maintenance treatments like corrosion protection, concrete repair, and beam-column joint retrofitting have the potential to raise the energy dissipative capacity by 30-40% and cost savings in life-cycle repair by as much as 25%. In addition, the new developments in sensor-based structural health monitoring and digital twin technology allow real-time performance monitoring for adaptive maintenance planning and seismic design optimisation. The research concludes that incorporation of maintenance concepts into seismic performance approach rejuvenates resilience evaluation as an active, data-driven process from a passive design-oriented process. The strategy facilitates sustainable infrastructure construction, maintains post-earthquake usability, and prolongs functional lifespan of skyscrapers.

The composite materials are widely used across industries however, these materials are prone to damages like cracking and delamination due to its complexity. The Electromechanical Impedance (EMI) technique offers a reliable non-destructive solution for detecting such damage using piezoelectric sensors and enabling effective structural health monitoring and enhancing safety and durability. This study explores the application of the EMI technique for monitoring damages in composite fibre concrete specimens. The specimens were prepared using Ordinary Portland Cement (OPC), fly ash, and polypropylene, glass fiber mixture, water, fine and coarse aggregates. The Piezoelectric sensors were employed to record conductance and susceptance signatures, enabling early detection and quantification of damages. The severity of damages were assessed using statistical indices such as Root Mean Square Deviation (RMSD), Mean Absolute Percentage Deviation (MAPD), and Correlation Coefficient (CC) revealing higher sensitivity. A notable leftward shift in EMI signatures with increasing damage was confirmed progressive structural degradation. Additionally, structural parameters equivalent stiffness and equivalent damping were evaluated, demonstrating a decrease in stiffness and an increase in damping with greater damage depth. Temperature effects on EMI responses were also investigated, necessitating compensation for reliable analysis. An Artificial Neural Network (ANN) model was trained using Levenberg-Marquardt (LM) algorithm and implemented to predict conductance values and damage depth. The developed ANN showed high accuracy, with strong agreement between experimental and predicted results. Overall, the findings confirm the EMI technique’s potential for SHM of composite fiber concrete and integration with machine learning for improved predictive its durability assessment.

This paper presents a real-time, output-only structural health monitoring framework that integrates damage-sensitive cepstral features with a streaming Long Short-Term Memory (LSTM) network for automated damage detection. Acceleration time histories are segmented into overlapping windows, converted into cepstral coefficients, and processed sequentially by a stacked LSTM architecture with state carry-over. This design preserves temporal dependencies while enabling low-latency inference suitable for continuous monitoring. The framework was evaluated under a strict zero-shot setting on the full-scale Z24 Bridge benchmark, in which no training or calibration data from the bridge were used. Our results show that the proposed approach can reliably discriminate staged damage states and track their progression using only vibration measurements. By combining a well-established spectral feature representation with a streaming sequence model, the study demonstrates a practical pathway toward deployable, data-driven monitoring systems capable of operating without retraining on each individual asset.