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Model updating of a bridge structure using vibration test data based on GMPSO and BPNN: case study

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Abstract

Model updating issues with high-dimensional and strong-nonlinear optimization processes are still unsolved by most optimization methods. In this study, a hybrid methodology that combines the Gaussian-white-noise-mutation particle swarm optimization (GMPSO), back-propagation neural network (BPNN) and Latin hypercube sampling (LHS) technique is proposed. In this approach, as a meta-heuristic algorithm with the least modification to the standard PSO, GMPSO simultaneously offers convenient programming and good performance in optimization. The BPNN with LHS establishes the meta-models for FEM to accelerate efficiency during the updating process. A case study of the model updating of an actual bridge with no distribution but bounded parameters was carried out using this methodology with two different objective functions. One considers only the frequencies of the main girder and the other considers both the frequencies and vertical displacements of typical points. The updating results show that the methodology is a sound approach to solve an actual complex bridge structure and offers good agreement in the frequencies and mode shapes of the updated model and test data. Based on the shape comparison of the main girder at the finished state with different objective functions, it is emphasized that both the dynamic and static responses should be taken into consideration during the model updating process.

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Acknowledgement

This work was supported by the National Natural Science Foundation of China (Grant No. 51438002), the research fund of Jiangsu Province Key Laboratory of Structure Engineering, China (Grant No. ZD1803), Natural Science Foundation of Suzhou University of Science and Technology (Grant No. XKQ2018008), Natural Science Foundation of Jiangsu Higher Education Institutions of China (Grant No. 19KJB560021), Science and Technology Project of Jiangsu Construction System (Grant No. 2020ZD07) and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.

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Authors and Affiliations

  1. Jiangsu Province Key Laboratory of Structure Engineering, Suzhou University of Science and Technology, Suzhou, 215011, China

    Zhiyuan Xia & Huiyuan Shi

  2. School of Civil Engineering, Southeast University, Nanjing, 210096, China

    Zhiyuan Xia & Aiqun Li

  3. Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China

    Aiqun Li

  4. College of Civil Engineering, Nanjing Forestry University, Nanjing, 210037, China

    Jianhui Li

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  1. Zhiyuan Xia
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  2. Aiqun Li
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  3. Huiyuan Shi
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Correspondence to Zhiyuan Xia.

Additional information

Supported by: National Natural Science Foundation of China under Grant No. 51438002, the research fund of Jiangsu Province Key Laboratory of Structure Engineering, China under Grant No. ZD1803, Natural Science Foundation of Suzhou University of Science and Technology under Grant No. XKQ2018008, Natural Science Foundation of Jiangsu Higher Education Institutions of China under Grant No. 19KJB560021, Science and Technology Project of Jiangsu Construction System under Grant No. 2020ZD07, Priority Academic Program Development of Jiangsu Higher Education Institutions

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Xia, Z., Li, A., Shi, H. et al. Model updating of a bridge structure using vibration test data based on GMPSO and BPNN: case study. Earthq. Eng. Eng. Vib. 20, 213–221 (2021). https://doi.org/10.1007/s11803-021-2015-x

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  • DOI: https://doi.org/10.1007/s11803-021-2015-x

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