An effective prediction method for bridge long-gauge strain under moving trainloads with experimental verification. (1st March 2023)

• Record Type: Journal Article
• Authors: Wu, Bitao; Lin, Zucai; Liang, Yuxiong; Zhou, Zhenwei; Lu, Huaxi
• Is Part Of: Mechanical systems and signal processing. Volume 186(2023)
• Publication Date: 2023-03-01
• Subjects: Structural health monitoring -- Long-gauge strain sensor -- Dynamic monitoring -- Railway bridge -- Fiber optic sensor; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2022.109855
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Accurately simulate the working principle of FBG sensor for bridges under high-speed train loads. Fast prediction of dynamic long-gauge strain response under high-speed trainloads. The local and global stiffness degradation of the bridge has obvious characteristics in the dynamic long-gauge strain response. Long-gauge strain envelope curve was used for evaluating bridge stiffness degradation. Abstract: This paper presents a dynamic long-gauge strain prediction method for high-speed railway bridges when fiber Bragg grating (FBG) sensors are used. First, a refined three-dimensional numerical model of a rail-track bridge is established to calculate long-gauge strain response under moving trainloads. Then, by comparing the experiment measured and predicted acceleration histories of the bridge, the correctness of the prediction method for the dynamic response of the high-speed railway bridge is verified. A novel approach has been put forward to calculate the long-gauge strain, which can quickly obtain the dynamic long-gauge strain of high-speed railway bridges without knowing the height of the neutral axis in the section covered by the sensor. Finally, comparative field tests were conducted to verify the accuracy of the dynamic long-gauge strain prediction approach, and a health monitoring system based on distributed long-gauge strain sensing technology was installed on a high-speed railway bridge. The comparison results show that the dynamic long-gauge strain of high-speed railway bridges can be effectively predicted in the FE model. Furthermore, an envelope curve composed of long-gauge strain peak values was used for evaluating bridge stiffness degradation, and the prediction results show that the high-speed railway bridges' local and global stiffness degradation can be reflected in long-gauge strain time history. The predicted method of long-gauge dynamic strain under train load described may attract engineers who use FBG sensors to monitor the health of high-speed railway bridges.