Operational Influence Line Identification of High-Speed Railway Bridge Considering Uncertainty of Train Load

Abstract

The bridge influence line (BIL) contains static information for each section of a bridge, which is an important tool for bridge design and condition evaluation. The current influence line identification procedure relies on periodic load testing using calibrated vehicles and always leads to long-time traffic disruption. To overcome this shortcoming and achieve online tracking of high-speed railway (HSR) bridge influence lines, this paper proposes an operational influence line identification approach for HSR bridges based on train-induced responses only. First, to consider the train load uncertainty during operation, a train load interval model is established based on field investigations. Then, the BIL intervals are determined by interval computations and accumulate to generate a continuously updated database. Finally, the influence line is identified from the BIL interval database using a binary classification algorithm. The proposed method is verified by an example of a three-span girder bridge, and a train–bridge interaction model is established to simulate the bridge responses induced by various train loads. The results show that the proposed approach has similar accuracy as the traditional load testing methods, which can achieve high-accuracy online tracking of HSR bridge influence lines.