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<p>In addressing the issues of large data volume and noise interference in nonlinear finite element model updating (FEMU) using time history response, a nonlinear FEMU method based on singular value decomposition (SVD) of time history response is proposed. First, the singular values obtained through SVD are used to characterize the original time history response. When the time history response data is contaminated by noise, the effective rank of singular values is determined based on the principle of minimizing fitting error. Second, a Kriging model that meets accuracy requirements is used instead of the finite element model for iterative optimization, improving the efficiency of FEMU. Finally, an optimization algorithm is used to determine the values of the parameters to be updated, thereby achieving the purpose of FEMU. Numerical examples and experimental cases demonstrate that the proposed FEMU method has good updating performance and robustness against noise.</p>

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Model updating method for nonlinear finite element model based on singular value decomposition of time history responses

  • 期刊论文

  • 发表日期：

    2026年5月29日

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  • Qinyao Peng,
  • Chenguang Yang

摘要

In addressing the issues of large data volume and noise interference in nonlinear finite element model updating (FEMU) using time history response, a nonlinear FEMU method based on singular value decomposition (SVD) of time history response is proposed. First, the singular values obtained through SVD are used to characterize the original time history response. When the time history response data is contaminated by noise, the effective rank of singular values is determined based on the principle of minimizing fitting error. Second, a Kriging model that meets accuracy requirements is used instead of the finite element model for iterative optimization, improving the efficiency of FEMU. Finally, an optimization algorithm is used to determine the values of the parameters to be updated, thereby achieving the purpose of FEMU. Numerical examples and experimental cases demonstrate that the proposed FEMU method has good updating performance and robustness against noise.