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

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.

Probabilistic Damage Detection of Long-Span Bridges Using Measured Modal Frequencies and Temperature

This paper aims to develop a new probabilistic monitoring-based framework for damage detection of long-span bridges, by eliminating the temperature effects from the measured modal frequencies, probabilistic modeling of modal frequencies using kernel density estimate, and detection damage using the control chart. A methodology is presented to address the issue of modal frequencies' non-normal distribution, which has been neglected in the past studies using the control chart to detect the modal frequencies' abnormality caused by structural damages. The efficiency of the proposed framework is validated through a case study of long-term monitoring data of a long-span suspension bridge. The results show that after elimination of the temperature effects, the selected modal frequencies are not normally distributed, while the Q statistics transferred from the modal frequencies follow the standard normal distribution. The abnormality of modal frequencies can be detected when the data points of the Q statistics exceed the limits of the control chart. Further, the control chart has sufficient sensitivity and thus can be used to detect minor abnormalities of the prototype bridge's modal frequencies. It is concluded that the proposed probabilistic monitoring-based framework offers an effective technique for structural health monitoring of long-span bridges.