Simultaneous identification of bridge structural parameters and vehicle loads. (September 2015)

• Record Type: Journal Article
• Authors: Feng, Dongming; Sun, Hao; Feng, Maria Q.
• Is Part Of: Computers & structures. Volume 157(2015)
• Page Start: 76
• Page End: 88
• Publication Date: 2015-09
• Subjects: Vehicle axle loads -- Moving forces -- Vehicle–bridge interaction -- Output-only identification -- Bayesian regularization -- Damped Gauss–Newton optimization; Structural engineering -- Data processing -- Periodicals; Electronic data processing -- Structures, Theory of -- Periodicals; 624.171
• DOI: 10.1016/j.compstruc.2015.05.017
• Languages: English
• ISSNs: 0045-7949

Abstract

Highlights: A time-domain method is proposed to simultaneously identify bridge structural parameters and vehicle axle loads. The estimation of unknown axle loads is incorporated in the framework of an iterative parametric optimization process. A Bayesian inference regularization is presented to solve the ill-posed least squares problem for input axle loads. Numerical analyses are conducted to validate the proposed method. The bridge dynamic response can be accurately predicted using the identified axle loads and structural parameters. Abstract: Most of the existing methods for 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 bridge structural parameters and vehicle dynamic axle loads of a 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 simply-supported single-span bridge and a three-span continuous bridge are conducted to investigate the accuracy and efficiency of the proposed method. Effects of the vehicle speed, the number of sensors, the measurement noise, and initial estimates of structural parameters on the accuracy of the identification results are investigated, demonstrating the robustness and efficiency of the proposed algorithm. Finally, it is shown that the bridge dynamic response can be accurately predicted using the identified axle load histories and structural parameters.