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Simultaneous identification of structural parameters and vehicle axle loads of the vehicle-bridge interaction system

Most of the existing methods for the identification of vehicle axle loads are based on a model with known system parameters. In this study, a new method is proposed to simultaneously identify the structural parameters and the dynamic axle loads of the vehicle-bridge interaction system from a limited number of response measurements. As an inverse output-only identification problem, the estimation of unknown axle loads is incorporated in the framework of an iterative parametric optimization process, wherein the objective is to minimize the error between the measured and predicted system responses. A Bayesian inference regularization is presented to solve the ill-posed least squares problem for input axle loads. Numerical analyses of a single-span simply supported bridge and a three-span continuous bridge are conducted to demonstrate the accuracy and efficiency of the proposed method. Effects of the vehicle speed, the sensor number and the measurement noise on the accuracy of the identification results are investigated. The results indicate that the proposed algorithm is robust and efficient for simultaneous identification of both structural parameters and vehicle axle loads. Finally, it is shown that dynamic structural responses could be accurately predicted using the identified axle load histories and system parameters.

Author(s):

Dongming Feng    
Columbia University
United States

Maria Feng    
Columbia University
United States

Hao Sun    
Department of Civil & Environmental Engineering, MIT
United States