Axle-information-free identification of vehicle moving forces from bridge displacement using U-Net

Abstract

Traffic loads are the main source of live loads on bridge structures, and accurately identifying the dynamic axle load of vehicles is of great importance for Bridge Structural Health Monitoring (BSHM). Most of the past research established the relationship between moving forces and structures through analytical or finite element methods to estimate axle loads, which was proved to be ill-conditioned and inefficient. In this paper, a Moving Force Identification (MFI) method based on U-shaped Network (U-Net) is proposed, which does not require axle position information as a prior knowledge, but is completely based on monitoring data. Firstly, the dynamic response of the finite element model under traffic load is used to establish a data set. In this process, a large number of random traffic flows with different axle loads, axle spacings and vehicle speeds are generated to simulate the operation process of real bridges. Then, the deep learning model is trained with dynamic displacement data as input and dynamic axial load as output. The accuracy of the model is verified by numerical simulation and the robustness of the model is tested. Finally, the practicality of the proposed method is tested by the actual bridge experiment. The results of the actual bridge test show that the mean error of the axle force identification of the proposed method is 6.28 %, indicating that the proposed method has certain application prospects in practical engineering.