Broad studies have addressed the issue of structural element damage identification, however, rubber bearing, as a key
component of load transmission between the superstructure and substructure, is essential to the operational safety of a bridge, which should be paid more attention to its health condition. However, regarding the limitations of the traditional bearing damage detection methods as well as few studies have been conducted on this topic, in this paper, inspired by the model updating-based structural damage identification, a two-stage bearing damage identification method has been proposed. In the first stage, we deduce a novel bearing damage localization indicator, called element relative MSE, to accurately determine the bearing damage
location. In the second one, the prior knowledge of bearing damage localization is combined with sailfish optimization (SFO) to perform the bearing damage estimation. In order to validate the feasibility, a numerical example of a 5-span continuous beam is introduced, also the noise robustness has been investigated. Meanwhile, the effectiveness and engineering applicability are further verified based on an experimental simply supported beam and actual engineering of the I-40 Bridge. The obtained results are good, which indicate that the proposed method is not only suitable for simple structures but also can accurately locate the
bearing damage site and identify its severity for complex structure. To summarize, the proposed method provides a good
guideline for the issue of bridge bearing detection, which could be used to reduce the difficulty of the traditional bearing failure detection approach, further saving labor costs and economic expenses.
Minshui Huang, Chang Sun, Chunyan Xiang, Zihao Wan, Jianfeng Gu: School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430073, China; Hubei Provincial Engineering Research Center for Green Civil Engineering Materials and Structures, Wuhan, 430073, China
Zhongzheng Ling: Tongji Architectural Design (Group) Co., Ltd., Shanghai 200092, China
Yongzhi Lei: Centre for Infrastructural Monitoring and Protection, School of Civil and Mechanical Engineering, Curtin University, Kent Street, Bentley, WA 6102, Australia
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