Drive-by damage detection methodology for high-speed railway bridges using sparse autoencoders

Abstract

High-speed railway bridges are essential components of any railway transportation system that should keep adequate levels of serviceability and safety. In this context, drive-by methodologies have emerged as a feasible and cost-effective monitoring solution for detecting damage on railway bridges while minimizing train operation interruptions. Moreover, integrating advanced sensor technologies and machine learning algorithms has significantly enhanced structural health monitoring (SHM) for bridges. Despite being increasingly used in traditional SHM applications, studies using autoencoders within drive-by methodologies are rare, especially in the railway field. This study presents a novel approach for drive-by damage detection in HSR bridges. The methodology relies on acceleration records collected from multiple bridge crossings by an operational train equipped with onboard sensors. Log-Mel spectrogram features derived from the acceleration records are used together with sparse autoencoders for computing statistical distribution-based damage indexes. Numerical simulations were performed on a 3D vehicle–track–bridge interaction system model implemented in Matlab to evaluate the robustness and effectiveness of the proposed approach, considering several damage scenarios, vehicle speeds, and environmental and operational variations, such as multiple track irregularities and varying measurement noise. The results show that the proposed approach can successfully detect damages, as well as characterize their severity, especially for very early-stage damages. This demonstrates the high potential of applying Mel-frequency damage-sensitive features associated with machine learning algorithms in the drive-by condition assessment of high-speed railway bridges.

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