Damage identification of bridge structures based on extended Kalman filter algorithm without prior knowledge of road roughness and moving vehicle

Abstract Identifying contact moving load and bridge damage based on the bridge vibration signals is a classic inverse problem. Meanwhile, the ill-poseness of the inverse problem is serious and the results may not converge due to the unknown vehicle parameters and road roughness. In this study, the transformation strategy of VBI model is used to eliminate the effects of unknown vehicle parameters and excitation, and then a new method is proposed to identify bridge structural damages and moving vehicle-bridge contact force step by step. In the each recursive step, the extended Kalman filter (EKF) method with \(l1\)-norm regularization are used to obtain the minimum variance estimation of moving contact force or bridge damages from a limited numbers of response measurements. Numerical analyses of a simply-supported bridge under the moving vehicle are conducted to investigate the accuracy and efficiency of the proposed method. Effects of the vehicle speed, the damage cases, the measurement noise, and the roughness levels on the accuracy of the identification results are investigated. The results demonstrate the robustness and efficiency of the proposed algorithm which can be developed into an effective tool for structural health monitoring of bridges.