Damage identification in bridge structures subject to moving vehicle based on extended Kalman filter with l1-norm regularization

• Published in: Inverse problems in science and engineering Vol. 28; no. 2; pp. 144 - 174
• Main Authors: Zhang, Chun, Gao, Yu-Wei, Huang, Jin-Peng, Huang, Jie-Zhong, Song, Gu-Quan
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 01.02.2020
• Subjects: Damage identification; extended Kalman filter; l1-norm regularization; moving vehicular; pseudo-measurement
• ISSN: 1741-5977; 1741-5985
• DOI: 10.1080/17415977.2019.1582650

An innovative damage detection method for bridge structures under moving vehicular load is proposed on the basis of extended Kalman filter (EKF) and l1-norm regularization. An augmented state vector includes structural damage parameters and motion state variables of bridge and vehicle. Through a recursive process of the EKF, the structural damage parameters and state variables of a bridge are updated continually to obtain an optimal estimate using bridge responses due to a moving vehicle. The distribution of element stiffness reduction of a structure with local damages is sparse. Thus, l1-norm regularization is introduced into the updating process of the EKF using pseudo-measurement (PM) technology to improve the ill-posedness of the inverse problem. Numerical studies on a simple-supported and continuous beam bridge deck, with a smooth road surface that is subject to a moving vehicle, are performed to test the proposed approach. Furthermore, using the robustness of the EKF, the proposed algorithm is applied as a simplified method to the case where a bridge deck with road roughness is considered. Results show that the proposed identification algorithm is robust and effective for different vehicle speeds and measurement noises under smooth and good road conditions.