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An efficient refracted salp swarm algorithm and its application in structural parameter identification

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Abstract

Efficient and accurate structural parameter identification is critical for the practical application of structural health monitoring. In this paper, a novel algorithm named refracted salp swarm algorithm (RSSA) is proposed and applied to identify structural parameters. Firstly, the basic salp swarm algorithm is improved by refracted opposition-based learning strategy, multi-leader mechanism and adaptive conversion parameter strategy. The superiority of the proposed algorithm is verified by experiments of eight benchmark functions of various types and dimensions. Secondly, a new type of structural parameter identification (SPI) model is established by combining RSSA and the Newmark integration method, which is mainly used to solve the optimization problem based on structural acceleration, thereby identifying structural parameters such as stiffness, mass and damping ratio. Numerical simulation test of seven-floor frame proves that the new proposed RSSA could be successfully applied in the SPI model. Compared with other heuristic algorithms, RSSA can obtain more accurate recognition results under the circumstances incomplete measurement data and low signal-to-noise ratio.

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Acknowledgements

This paper is supported by the National Natural Science Foundation of China (No. 41404008), the Guiding Project of Fujian Science and Technology Program (No. 2018Y0021) and Open Foundation of Key Laboratory for Digital Land and Resources of Jiangxi Province (No. DLLJ201911).

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Authors and Affiliations

  1. College of Civil Engineering, Fuzhou University, Fuzhou, 350116, China

    Qian Fan, Zhenjian Chen, Zhanghua Xia & Youqin Lin

  2. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, 999077, Hong Kong, China

    Zhao Li

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  1. Qian Fan
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  4. Zhanghua Xia
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Correspondence to Qian Fan.

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Fan, Q., Chen, Z., Li, Z. et al. An efficient refracted salp swarm algorithm and its application in structural parameter identification. Engineering with Computers 38, 175–189 (2022). https://doi.org/10.1007/s00366-020-01034-7

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