Track Irregularity Monitoring on High-Speed Railway Viaducts: A Novel Algorithm with Unknown Input Condensation

Abstract

The inverse dynamic analysis of vehicle-bridge (VB) systems provides a superior scheme for track irregularity identification using the onboard measurement data of vehicle dynamic responses. However, available algorithms face a great challenge when applied to high-speed railway bridges because of the increase in the number of unknown inputs, resulting in a degradation of identification accuracy. Here, we propose a condensation procedure to reduce the unknown inputs of the VB system, and then propose a novel algorithm for track irregularity identification using the augmented Kalman filter. A numerical simulation of a real continuous three-span simply supported railway bridge was conducted to validate the estimation accuracy of the proposed algorithm. Numerical results illustrate that the proposed algorithm can accurately estimate the track irregularities by using the vertical acceleration responses of the vehicle body only, thanks to the unknown input condensation. A comparative study demonstrates that the proposed algorithm outperforms the conventional track irregularity identification methods in terms of estimation accuracy. To evaluate the actual performance of the proposed algorithm, a set of onboard measurement data recorded by a typical high-speed train when running on the Hangzhou–Changsha high-speed railway in China was employed to identify the track irregularities of the railway line, and the results suggest that the proposed algorithm is highly efficient and low in cost. This study may help develop highly efficient real-time track irregularity monitoring in the years ahead.