Aug 2026· e-Journal of Nondestructive Testing· Vol 31· 0 citations
TL;DR
Results show that multi-year monitoring data can be reduced into compact fatigue-relevant features while preserving traceability to raw measurements, and a supervisory agentic layer coordinates data-quality checks, multi-sensor consistency review, and confidence-tagged substitution, creating an auditable workflow for engineering decision support.
Abstract
Ensuring the integrity of critical infrastructure, such as bridges, dams, and large-scale structures, is essential to safety, reliability, and operational continuity. These assets are exposed to mechanical, thermal, environmental, and operational loads that can accelerate fatigue, corrosion, wear, and other deterioration mechanisms. Traditional inspections are periodic and costly, while sensor-based monitoring systems generate large volumes of imperfect field data that are difficult to interpret without engineering context.
This paper presents a physics-informed machine learning and multi-sensor fusion framework for fatigue-oriented Structural Health Monitoring (SHM) and predictive maintenance. The method is demonstrated on an instrumented steel-concrete composite bridge using resistive strain, fiber Bragg grating (FBG) strain and temperature, and displacement measurements. In decision-critical SHM applications, uncertainty arises from noisy sensor signals, thermal effects, drift, missing data, and ambiguous operating events. The proposed framework reduces this uncertainty by applying physics and engineering principles to structure the correction, validation, and interpretation of sensor data.
Machine learning supports thermal compensation, anomaly screening, cross-sensor substitution, and confidence tagging, while deterministic engineering methods remain responsible for strain interpretation, event extraction, rainflow-style cycle counting, and fatigue damage indicators. Results show that multi-year monitoring data can be reduced into compact fatigue-relevant features while preserving traceability to raw measurements. A supervisory agentic layer coordinates data-quality checks, multi-sensor consistency review, and confidence-tagged substitution, creating an auditable workflow for engineering decision support. SHM value does not come only from better sensors or better ML models. It comes from traceable workflows that turn imperfect field data into reliable, reviewable, and actionable engineering evidence.
Structural integrity assessment is essential for ensuring the safety and reliability of critical infrastructure such as bridges, buildings, dams, tunnels, pipelines, and power plants. Traditional Structural Health Monitoring (SHM) methods rely on either physics-based simulations or data-driven machine learning, each having limitations in computational cost, data requirements, and generalization. Physics-Informed Machine Learning (PIML) integrates physical laws with deep learning to improve damage detection, structural condition assessment, and predictive maintenance using limited and noisy sensor data. This paper reviews recent PIML approaches, including PINNs, hybrid finite element–AI models, digital twins, and uncertainty-aware learning, while identifying key research challenges. A generalized PIML framework is proposed to support accurate, explainable, and intelligent structural health monitoring for resilient next-generation infrastructure.
John Backus, G. Hopper· International Journal of Mod...· 0 citations
Developing reliable health indicators (HIs) for aeronautical composite structures is challenging because damage evolution is complex, stochastic, and affected by uncertainties such as impact events and manufactured defects. In this context, passive and active structural health monitoring (SHM) techniques provide complementary information: acoustic emission (AE) captures temporally dense signatures of damage activity, whereas guided waves (GW) provide state-sensitive interrogation of the structure at discrete inspection times. This work therefore investigates heterogeneous sensor fusion for HI construction in composite T-stiffener panels subjected to run-to-failure compression-compression fatigue loading. Modality-specific AE- and GW-based HI frameworks are first developed independently using signal processing and AI. The resulting HIs are then synchronized in the fatigue-cycle domain and combined through inter-modality fusion to obtain a single fused HI. The study addresses key challenges including the absence of true HI labels, synchronization of passive and active measurements, and learning from limited multimodal overlap. The results show the potential of combining AE and GW to construct more informative and robust HIs for prognostics of composite aerostructures.
Morteza Moradi, D. Zarouchas· e-Journal of Nondestructive...· 0 citations
Aging steel railway bridges accumulate fatigue damage at locations that periodic inspection rarely reaches and that conventional vibration monitoring detects only after severe stiffness loss. Existing physics-informed learning methods for structural health monitoring (SHM) are mostly validated on synthetic data and single snapshots, leaving their performance on continuous field data with verified damage untested. This study develops a strain-based monitoring approach, supported by a physics-informed neural network (PINN), for early detection of fatigue damage in a steel railway bridge. The measured strain is compensated for temperature using a model calibrated on healthy-state data, the resulting anomaly is tracked through a statistical control chart, and a PINN embedding the Euler–Bernoulli equation and the strain–stiffness relationship converts the sparse measurements into a continuous, physically valid flexural-stiffness field, identified through two-stage training. The framework was evaluated on 56 ambient recordings spanning 19 days across an inspection-confirmed fatigue crack in the Vänersborg railway bridge, Sweden, using only three strain gauges (SG9, SG10, SG11) and one accelerometer (A5). The main findings are: the framework issued a warning 15 days before the in-service monitoring system; the identified stiffness reduction concentrated in the region of the crack-adjacent gauge, consistent with the inspection finding; the identified stiffness field tracked the progression of the damage as a normalized reduction relative to the healthy baseline; and perfect detection was achieved under a persistence rule with zero false alarms. A co-located accelerometer recorded a 94-fold spike at crack propagation but gave no prior warning, confirming that the early detection derives from the temperature-corrected strain identification. The framework provides a reproducible, low-cost, and interpretable basis for proactive bridge maintenance.
A. Khan, Ali Raza, A. Pimanmas· Intelligent Transportation I...· 0 citations
Abstract. High-performance mechanical component structural health monitoring (SHM) is a vital issue in contemporary engineering, especially in the aerospace, automotive, and industrial turbomachinery sectors where component failure may be disastrous. This article introduces a new AI-aided SHM framework with multimodal sensor fusion and ensemble deep learning architecture with one-dimensional convolutional neural networks (1D-CNN) and bidirectional long short-term memory (Bi-LSTM) networks, which can be used to detect fault, classify fault, and predict remaining useful life (RUL) in real-time. The proposed system takes in time-series streams of vibration, acoustic emission, and strain gauge data, runs them through an adaptive signal preprocessing pipeline, and derives hierarchical features of fault relevance without using manually specified features. The tests are done on two benchmark datasets, which include the CWRU bearing fault dataset and a custom gas turbine blade fatigue dataset that were obtained under controlled laboratory settings and a real gas turbine compressor testbed. The CNN-BiLSTM ensemble suggested has an accuracy of fault classification of 98.7 and a mean absolute percentage error (MAPE) of 3.14 to predict RUL with average inference latency of 12.3 ms, which is appropriate to be integrated into embedded systems in real-time. These findings constitute a statistically significant step forward compared to the current state-of-the-art baselines and they generalize and scale to provide an AI-SHM paradigm of safety-critical mechanical systems.
Jasjeet Singh· Materials Research Proceedin...· 0 citations
Metallurgical cranes have a high risk of structural fatigue damage and failure under complex working conditions such as high temperature, heavy load, and strong electromagnetic interference. This article proposes a data-driven structural fatigue damage health monitoring system. This system integrates fiber Bragg grating sensing technology, rigid flexible coupling multi-body dynamics simulation, and big data analysis methods to construct a sensor optimization layout strategy based on rigid flexible coupling virtual prototype simulation, achieving real-time perception of stress states in key parts such as the mid span and end beam corners of the main beam. Develop a visualization system that integrates health monitoring, damage diagnosis, and life prediction. This system can dynamically evaluate the structural health status of metallurgical cranes and predict the remaining life of the structure based on a nonlinear cumulative damage model. On site engineering applications have shown that the monitoring and prediction visualization system can effectively improve the intelligent and safe operation and maintenance level of metallurgical cranes, providing a data foundation and possibility for their predictive maintenance.
Li Chen, Xu Zhang, Ke-Qin Ding· SAE technical paper series· 0 citations
Structural health monitoring (SHM) plays a critical role in the early identification and assessment of structural damage, thereby enhancing the safety and reliability of civil infrastructure. Structural damage identification generally encompasses three key tasks: damage detection, localization, and quantification. While extensive research has been conducted on each of these tasks individually, relatively few studies have integrated all three components into a unified framework for comprehensive structural condition assessment. Physics-based approaches require an accurate finite element model (FEM), which is often difficult to calibrate to accurately represent the behavior of the actual structure. In contrast, data-driven approaches rely on sufficient labeled data collected from the actual structure, which is likewise challenging to acquire in practice. To address these limitations, this work proposes an integrated hierarchical physics-informed domain adaptation (I-HierPhyDA) framework that performs damage detection, localization, and severity classification in a hierarchical manner. The proposed framework bridges the gap between the reduced-order FEM and the higher-fidelity FEM by generating vibration signatures that are consistent across both domains. Furthermore, the proposed framework enables structural damage localization without requiring damage-state data from the target domain during training, while damage severity classification is performed using transductive domain adaptation with unlabeled damaged-state data from the target domain. The proposed framework is systematically evaluated using the numerical ASCE benchmark models under structural uncertainties and measurement noise. The results demonstrate that the proposed approach achieves accurate structural damage detection and localization. For structural damage severity classification, it achieves the highest mean accuracy and Macro-F1 score while exhibiting the lowest standard deviations for both metrics among the baseline and ablation methods, demonstrating its effectiveness for comprehensive structural condition assessment. Future work will focus on experimentally validating the proposed approach using measured data from laboratory or field structures.
Zixin Wang, Mohammad R. Jahanshahi· Italian National Conference...· 0 citations
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