PSI - Issue 64

ScienceDirect Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2023) 000–000 Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2023) 000–000 Available online at www.sciencedirect.com Procedia Structural Integrity 64 (2024) 500–506

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SMAR 2024 – 7th International Conference on Smart Monitoring, Assessment and Rehabilitation of Civil Structures Drive-by modal identification of high-speed railway bridge via CP SMAR 2024 – 7th International Conference on Smart Monitoring, Assessment and Rehabilitation of Civil Structures Drive-by modal identification of high-speed railway bridge via CP response identification JiantaoLi a,b,c* 一 , Xuanrui Pan a response identification JiantaoLi a,b,c* 一 , Xuanrui Pan a a Zhejiang University of Technology, Hangzhou 310000, Zhejiang, China. b University of Science and Technology of China, Hefei 230026, Anhui, China c Zhejiang Zhonghe Architectural Design Co., LTD, Shaoxing 312000, Zhejiang, China Abstract Railway bridges are subjected to heavy operational loads and significant impact forces due to the moving trains. It is important to carry out an effective structural health monitoring to ensure the safe operation of railway bridges. Traditional online bridge monitoring methods require sensor installations on the structures that can be time consuming and expensive making it very difficult to satisfy the huge needs of monitoring all the existing railway bridges. Therefore, this study proposes to use the drive-by method for the bridge modal identification using responses of instrumented in-service trains. The measuring sensors are installed on the train that can conduct the measurement in its normal operational states. To improve the feasibility and accuracy of drive-by bridge modal identification, the contact-point (CP) responses between the wheels and the rail track are identified from the train responses. A novel method via Bayesian expectation-maximization based augmented Kalman filter is proposed to reconstruction the CP responses and unknown states of the train simultaneously. The method is robust to the measurement noise of the train responses and the CP responses can be identified accurately. The identified CP responses successfully eliminate the dynamic components related to the train and greatly enhance the bridge dynamic information. The proposed CP response reconstruction method has great potential for drive-by bridge modal identification using the responses of train that can be further used to assess the structural condition of high speed railway bridges. Abstract Railway bridges are subjected to heavy operational loads and significant impact forces due to the moving trains. It is important to carry out an effective structural health monitoring to ensure the safe operation of railway bridges. Traditional online bridge monitoring methods require sensor installations on the structures that can be time consuming and expensive making it very difficult to satisfy the huge needs of monitoring all the existing railway bridges. Therefore, this study proposes to use the drive-by method for the bridge modal identification using responses of instrumented in-service trains. The measuring sensors are installed on the train that can conduct the measurement in its normal operational states. To improve the feasibility and accuracy of drive-by bridge modal identification, the contact-point (CP) responses between the wheels and the rail track are identified from the train responses. A novel method via Bayesian expectation-maximization based augmented Kalman filter is proposed to reconstruction the CP responses and unknown states of the train simultaneously. The method is robust to the measurement noise of the train responses and the CP responses can be identified accurately. The identified CP responses successfully eliminate the dynamic components related to the train and greatly enhance the bridge dynamic information. The proposed CP response reconstruction method has great potential for drive-by bridge modal identification using the responses of train that can be further used to assess the structural condition of high speed railway bridges. Keywords: Modal identification; vehicle-bridge interaction; CP response; Augmented Kalman filter; Bayesian expectation-maximization © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SMAR 2024 Organizers a Zhejiang University of Technology, Hangzhou 310000, Zhejiang, China. b University of Science and Technology of China, Hefei 230026, Anhui, China c Zhejiang Zhonghe Architectural Design Co., LTD, Shaoxing 312000, Zhejiang, China

Keywords: Modal identification; vehicle-bridge interaction; CP response; Augmented Kalman filter; Bayesian expectation-maximization

一 * Corresponding author. E-mail address: jiantaoli@zjut.edu.cn

2452-3216 © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SMAR 2024 Organizers 10.1016/j.prostr.2024.09.293 2452-3216 © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SMAR 2024 Organizers 2452-3216 © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SMAR 2024 Organizers 一 * Corresponding author. E-mail address: jiantaoli@zjut.edu.cn