Condition Monitoring and Quantitative Evaluation of Railway Bridge Substructures Using Vehicle-Induced Vibration Responses by Sparse Measurement

Abstract

Bridge substructure failure has been responsible for numerous recorded bridge collapses, particularly for small- and medium-span bridges, so it is crucial to effectively monitor the performance of the bridge substructures for efficient maintenance and management. The current vibration-based approaches for quantitatively evaluating bridge substructures rely on in-situ experiments with a multitude of sensors or impact vibration test, making it challenging to implement long-term online monitoring. This paper proposes an accurate, low cost, and practicable method to achieve online quantitative monitoring of railway bridge substructures using only one vibration sensor and operational train-induced vibration responses. The newly derived flexible-base Timoshenko beam models, along with the random decrement technique and Levenberg–Marquardt–Fletcher algorithm, are employed to identify the modal parameters and quantitatively assess the condition of bridge substructures. The proposed method is numerically verified through an established 3D train-bridge-foundation coupling system considering different damage scenarios. In addition, a real-world application is also conducted on the 2^nd Songhua River bridge in the Harbin–Dalian high-speed railway, aiming at examining the effectiveness and robustness of the method in condition monitoring of bridge substructure under a complete freeze-thaw cycle. The results indicate that the proposed methodology is effective in extracting the modal parameters and monitoring the state evolution of the bridge substructures, which offers an efficient and accurate strategy for condition monitoring and quantitative evaluation of railway bridge substructures.