Load Identification of High-Speed Train Crossbeam Based on Bayesian Finite Element Model Updating and Load-Strain Linear Superposition Algorithm

Abstract

The load identification of the crossbeams in high-speed trains is proving to be crucial, because of the complexity of its structure and environment. In this paper, a method to identify load of the crossbeam structure based on Bayesian finite element model updating and load-strain linear superposition algorithm is proposed. Firstly, the finite element model is established. Using the numerical simulation under different loads, the relative mean error of load identification is less than 0.5% and the effect of strain field reconstruction performs well. Based on the model test system, Markov Chain Monte Carlo method is used to update the parameters of the initial finite element model. Then an experimental setup is built with 64 FBG sensors installed on the crossbeam. The load identification results of Tikhonov regularization method, the load-strain linear superposition method before and after the Bayesian finite element model updating are analyzed and compared. The results show that the proposed method can significantly improve the accuracy of load identification.