Spatio-Temporal Frequency Evaluation of a Railroad Bridge Considering Vehicle-Bridge Interaction

Abstract

Within a railroad bridge, due to the massive and directionally moving vehicle, interaction dynamics between the vehicle and the bridge systems become considerable. Such interaction induces the temporal variation of the fundamental frequency for both systems, resulting structural inspection using conventional approaches challenging. Thus, the objective of the presented study is to develop an algorithm that evaluates the real-time spatio-temporal frequency variation of a railroad bridge targeting to be implemented on an unmanned ground vehicle. So far, various studies revealed that the critical factors in frequency variation are the mass ratio and the vehicle stiffness. Temporal eigenvalue analysis provides the simplest solutions when the bridge’s first mode interaction is of interest. However, when the higher bridge modes and the multiple-vehicle interaction are of concern, eigenvalue solution may generate sorting errors. Thereby achieving theoretical solution for spatio-temporal frequency variation is challenging. Instead, Time-Frequency Analysis (TFA) can be adopted as an alternative method. In this work, Modified Stockwell Transform (MST) is used as a TFA tool which has flexible window for obtaining the optimized spatio-temporal energy distribution representations. By constructing the MST database of a single vehicle interaction problem, a conditional generative adversarial network based deep-learning algorithm is developed to extract multi-vehicle interaction frequencies with different bridge modes. The mass ratio and vehicle stiffness are varied to ensure the integrity of the database. Subsequently, lab-scale experiments are performed to demonstrate that MST map can be achieved experimentally. Proposed algorithm provides an effective tool for the railroad bridge fundamental characteristic identifications. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.