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A novel multistage approach for structural model updating based on sensitivity ranking

  • Jiang, Yufeng (Shandong Provincial Key Laboratory of Ocean Engineering, Ocean University of China) ;
  • Li, Yingchao (College of Civil Engineering, Ludong University) ;
  • Wang, Shuqing (Shandong Provincial Key Laboratory of Ocean Engineering, Ocean University of China) ;
  • Xu, Mingqiang (Shandong Provincial Key Laboratory of Ocean Engineering, Ocean University of China)
  • Received : 2019.04.19
  • Accepted : 2020.01.14
  • Published : 2020.06.25

Abstract

A novel multistage approach is developed for structural model updating based on sensitivity ranking of the selected updating parameters. Modal energy-based sensitivities are formulated, and maximum-normalized indices are designed for sensitivity ranking. Based on the ranking strategy, a multistage approach is proposed, where these parameters to be corrected with similar sensitivity levels are updated simultaneously at the same stage, and the complete procedure continues sequentially at several stages, from large to small, according to the predefined levels of the updating parameters. At every single stage, a previously developed cross model cross mode (CMCM) method is used for structural model updating. The effectiveness and robustness of the multistage approach are investigated by implementing it on an offshore structure, and the performances are compared with non-multistage approach using numerical and experimental vibration information. These results demonstrate that the multistage approach is more effective for structural model updating of offshore platform structures even with limited information and measured noise. These findings serve as a preliminary strategy for structural model updating of an offshore platform in service.

Keywords

Acknowledgement

This work was supported by the National Science Fund for Distinguished Young Scholars (51625902), the National Key Research and Development Program of China (2019YFC0312404), the Major Scientific and Technological Innovation Project of Shandong Province (2019JZZY010820), the National Natural Science Foundation of China (51809134), the Natural Science Foundation of Shandong Province (ZR2017MEE007), and the Taishan Scholars Program of Shandong Province (TS201511016).

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