The structural integrity of railway rails is essential for the safety and efficiency of modern transportation networks, where early detection of damage is crucial to preventing catastrophic failures and service disruptions. Guided wave-based structural health monitoring (SHM) offers long-range and high-sensitivity inspection capabilities for rail infrastructure. However, the complex propagation characteristics of ultrasonic guided waves and the presence of noise in operational environments pose significant challenges for traditional signal processing methods. In this study, a deep learning-based framework is proposed for rail damage detection utilizing guided wave SHM. The methodology involves denoising, normalizing, and transforming the acquired ultrasonic signals into time–frequency representations, which, together with raw waveforms, are used as inputs to a long short-term memory (LSTM) network. The LSTM model is designed to automatically learn temporal dependencies and extract discriminative features for accurate damage identification. The proposed approach achieves superior detection accuracy compared to conventional techniques and maintains robustness under elevated noise conditions. These findings underscore the potential of integrating deep learning with guided wave SHM for intelligent and automated rail defect detection, paving the way for scalable monitoring solutions and enhanced railway infrastructure reliability.
Structural Health Monitoring (SHM) using ultrasonic-guided waves (UGWs) enables continuous monitoring of components with complex geometries and provides detailed information about their structural integrity and overall condition. Due to their intricated characteristics, UGWs are…
Aging civil structures are increasingly vulnerable to environmental degradation and natural hazards, highlighting the need for reliable and automated structural health monitoring (SHM) systems. This study proposes a novel SHM framework that integrates Multiscale Increment Entropy…
TL;DR: This work demonstrates that the combined use of nonlinear acoustics, acoustic emission, and machine learning constitutes a robust and highly sensitive SHM framework for composite structures.
This paper presents an integrated Structural Health Monitoring (SHM) strategy for flax fiber reinforced thermoplastic composites, combining Nonlinear Resonance Acoustic Spectroscopy (NLRAS), Acoustic Emission (AE), and data-driven damage identification based on machine learning.…
TL;DR: A framework for CVSHM, which allows for robust detection, localization, and assessment of the damage even for highly contaminated displacement data, is proposed and tested using realistic synthetic videos representing vibrating truss structure.
Computer-vision-based structural health monitoring (CVSHM) enables contactless displacement measurement at multiple locations on the vibrating structure. Additionally, such a measurement can be realized from a certain distance from the monitored infrastructure. It provides a poss…
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.
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…
Load identification is a crucial topic in structural health monitoring (SHM). Existing approaches involve a trade-off between the amount of data required and the fidelity of available parametric physical models. Purely data-driven methods require extensive labeled training data f…