This research demonstrates predictive maintenance using acceleration data for early structural damage detection in railway infrastructure, suggesting enhanced monitoring capabilities.
Accurate structural damage prediction is essential for the effective maintenance of railway bridges. This study investigates whether long-term acceleration data from high-speed trains on Belgium’s Line 1 can be used to detect structural damage in earlier stages. Finite element modeling (FEM) is employed to simulate dynamic bridge responses under different damage scenarios and to analyze changes in eigenfrequencies. The results indicate that while acceleration data alone has limitations, eigenfrequency analysis shows promise for early damage detection. These insights contribute to the advancement of data-driven structural health monitoring for railway infrastructure.
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Beirendonck et al. (2025) studied this question.
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