Time-varying reliability analysis of nonlinear stochastic dynamic systems based on generalized subset simulation and adaptive Kriging model

被引:0
|
作者
Tang H. [1 ,2 ]
Guo X. [1 ]
Xue S. [1 ]
机构
[1] Department of Disaster Mitigation for Structures, Tongji University, Shanghai
[2] State Key Lab of Disaster Reduction in Civil Engineering, Tongji University, Shanghai
来源
关键词
AK-MCS method; Generalized subset simulation (GSS); Non-stationary random excitation; Nonlinear stochastic dynamic system; Time-varying reliability;
D O I
10.13465/j.cnki.jvs.2021.21.007
中图分类号
学科分类号
摘要
Here, aiming at time-varying reliability problem of nonlinear stochastic dynamic systems under the excitation of nonstationary Gaussian random process, a highly efficient calculation method GSS-AK-MCS of generalized subset simulation (GSS) and adaptive Kriging model combined with Monte Carlo adaptive updating (AK-MCS) was proposed. Based on the total probability theorem, the time-varying reliability of nonlinear stochastic dynamic system was converted into a two-layer nested problem. In inner layer, cumulative failure probability under non-stationary random excitation was calculated with GSS algorithm. In outer layer, Kriging agent model between system random parameters and cumulative failure probability was constructed adaptively. The reliability analysis of the stochastic system was realized based on the agent model. Taking time-varying reliability analyses of two nonlinear structure systems as examples, the feasibility of the proposed method was verified. The numerical results showed that GSS-AK-MCS method is not affected by spectral characteristics of non-stationary random excitation; compared with the traditional MCS and Kriging model method, the proposed method can significantly improve the calculation efficiency for time-varying reliability of nonlinear stochastic dynamic systems. © 2021, Editorial Office of Journal of Vibration and Shock. All right reserved.
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页码:47 / 54
页数:7
相关论文
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