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A Kriging-assisted sampling method for reliability analysis of structures with hybrid uncertainties

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  • Xiao, Mi
  • Zhang, Jinhao
  • Gao, Liang

Abstract

In this paper, subset simulation importance sampling (SSIS) method is extended for reliability analysis of structures with mixed random and interval variables. To improve its efficiency in cases with computationally expensive performance functions, an efficient Kriging-assisted SSIS method is proposed. In this method, Kriging metamodel is employed to substitute the actual performance function to decrease the number of its evaluations. Furthermore, it is determined that only the samples in the last level of SSIS are involved in the calculation of failure probability bounds. Then, from these samples, an update strategy based on measuring the possibility of correctly predicting the sign of performance function is developed to obtain update points, which are employed in the sequential refinement of Kriging metamodel. Additionally, metamodel uncertainty of Kriging is quantified and then considered in the termination criteria of Kriging update. The computational efficiency, accuracy and robustness of the proposed method is elucidated by its comparison with some existing methods in implementing the reliability analysis of six examples.

Suggested Citation

  • Xiao, Mi & Zhang, Jinhao & Gao, Liang, 2021. "A Kriging-assisted sampling method for reliability analysis of structures with hybrid uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    • Handle: RePEc:eee:reensy:v:210:y:2021:i:c:s0951832021001071
    DOI: 10.1016/j.ress.2021.107552
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    References listed on IDEAS

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    Cited by:

    1. Ouyang, Linhan & Che, Yushuai & Park, Chanseok & Chen, Yuejian, 2024. "A novel active learning Gaussian process modeling-based method for time-dependent reliability analysis considering mixed variables," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    2. Li, Shen & Kim, Do Kyun & Benson, Simon, 2021. "A probabilistic approach to assess the computational uncertainty of ultimate strength of hull girders," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    3. Meng, Zeng & Zhao, Jingyu & Chen, Guohai & Yang, Dixiong, 2022. "Hybrid uncertainty propagation and reliability analysis using direct probability integral method and exponential convex model," Reliability Engineering and System Safety, Elsevier, vol. 228(C).

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