Sensor data-based probabilistic monitoring of time-history deflections of railway bridges induced by high-speed trains

Abstract

Structural condition monitoring of railway bridges has been emphasized for guaranteeing the passenger comfort and safety. Various attempts have been made to monitor structural conditions, but many of them have focused on monitoring dynamic characteristics in frequency domain representation which requires additional data transformation. Occurrence of abnormal structural responses, however, can be intuitively detected by directly monitoring the time-history responses, and it may give information including the time to occur the abnormal responses and the magnitude of the dynamic amplification. Therefore, this study suggests a new Bayesian method for directly monitoring the time-history deflections induced by high-speed trains. To train the monitoring model, the data preprocessing of speed estimation and data synchronization are conducted first for the given training data of the raw time-history deflection; the Bayesian inference is then introduced for the derivation of the probability-based dynamic thresholds for each train type. After constructing the model, the detection of the abnormal deflection data is proceeded. The speed estimation and data synchronization are conducted again for the test data, and the anomaly score and ratio are estimated based on the probabilistic monitoring model. A warning is generated if the anomaly ratio is at an unacceptable level; otherwise, the deflection is considered as a normal condition. A high-speed railway bridge in operation is chosen for the verification of the proposed method, in which a probabilistic monitoring model is constructed from displacement time-histories during train passage. It is shown that the model can specify an anomaly of a train-track-bridge system.

Deformation Analysis and Prediction of a High-Speed Railway Suspension Bridge under Multi-Load Coupling

“…The effectiveness of the predicted value of deformation as a fundamental early-warning threshold has been confirmed [14,15]. However, to provide highly accurate deformation prediction values, it becomes imperative to conduct a comprehensive and precise analysis of load responses based on the measured data, followed by the establishment of a high-precision deformation prediction model [16].…”

“…Unfortunately, such considerations have been infrequently addressed in prior investigations. (ii) Unlike conventional long-span railway bridge monitoring, which predominantly focuses on stress, HSRSB monitoring prioritizes passenger comfort and track geometry maintenance [16,23]. Increased train speed generates greater impact forces and vertical displacement, thereby reducing train operational safety [24].…”