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Bayesian model update for damage detection of a steel plate girder bridge

  • Xin Zhou (Department of Civil and Earth Resources Engineering, Kyoto University) ;
  • Feng-Liang Zhang (School of Civil and Environmental Engineering, Harbin Institute of Technology) ;
  • Yoshinao Goi (Department of Civil and Earth Resources Engineering, Kyoto University) ;
  • Chul-Woo Kim (Department of Civil and Earth Resources Engineering, Kyoto University)
  • Received : 2021.11.26
  • Accepted : 2022.08.28
  • Published : 2023.01.25
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Abstract

This study investigates the possibility of damage detection of a real bridge by means of a modal parameter-based finite element (FE) model update. Field moving vehicle experiments were conducted on an actual steel plate girder bridge. In the damage experiment, cracks were applied to the bridge to simulate damage states. A fast Bayesian FFT method was employed to identify and quantify uncertainties of the modal parameters then these modal parameters were used in the Bayesian model update. Material properties and boundary conditions are taken as uncertainties and updated in the model update process. Observations showed that although some differences existed in the results obtained from different model classes, the discrepancy between modal parameters of the FE model and those experimentally obtained was reduced after the model update process, and the updated parameters in the numerical model were indeed affected by the damage. The importance of boundary conditions in the model updating process is also observed. The capability of the MCMC model update method for application to the actual bridge structure is assessed, and the limitation of FE model update in damage detection of bridges using only modal parameters is observed.

Keywords

Acknowledgement

This study was partly supported by a Japanese Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research (B) under project No. 19H02225 and National Natural Science Foundation of China under project No. 52278298. The financial supports are gratefully acknowledged.

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