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Hybrid reliability analysis with incomplete interval data based on adaptive Kriging

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  • Xiao, Tianli
  • Park, Chanseok
  • Lin, Chenglong
  • Ouyang, Linhan
  • Ma, Yizhong

Abstract

Hybrid reliability analysis with mixed random and interval uncertainties is a significant challenge in the reliability assessment of engineering structures. The situation will be more intractable when involving incomplete interval data. To obtain reliable estimates of the failure probability limits, an effective parameter estimation method, integrating the quantile variant of the Expectation-Maximization algorithm and Kullback-Leibler divergence, is proposed to transform uncertain variables with incomplete data into random variables with distribution uncertainty. Then, an adaptive Kriging-assisted hybrid reliability analysis method is developed to ensure computational accuracy and efficiency. In this method, a candidate pool incorporating the distribution uncertainty is constructed and its size is adaptively reduced by removing the samples that violate the projection uniformity on input dimensions with exact distributions. Meanwhile, an improved U learning function and an error-based convergence criterion are defined to drive and stop the adaptive process. Then the failure probability limits are estimated by combining the refined Kriging model and Monte Carlo simulation. Four application examples are employed to verify the superiority of the proposed method. Comparison results show that the proposed method can significantly improve computational efficiency while ensuring the accuracy and reliability of the estimated failure probability interval under incomplete interval observations.

Suggested Citation

  • Xiao, Tianli & Park, Chanseok & Lin, Chenglong & Ouyang, Linhan & Ma, Yizhong, 2023. "Hybrid reliability analysis with incomplete interval data based on adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
  • Handle: RePEc:eee:reensy:v:237:y:2023:i:c:s0951832023002764
    DOI: 10.1016/j.ress.2023.109362
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    References listed on IDEAS

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    1. Chang, Qi & Zhou, Changcong & Wei, Pengfei & Zhang, Yishang & Yue, Zhufeng, 2021. "A new non-probabilistic time-dependent reliability model for mechanisms with interval uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. 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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    4. Wang, Lei & Liu, Yaru & Li, Min, 2022. "Time-dependent reliability-based optimization for structural-topological configuration design under convex-bounded uncertain modeling," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    5. Chengning Zhou & Ning-Cong Xiao & Ming J Zuo & Xiaoxu Huang, 2020. "AK-PDF: An active learning method combining kriging and probability density function for efficient reliability analysis," Journal of Risk and Reliability, , vol. 234(3), pages 536-549, June.
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    Cited by:

    1. Zhang, Yu & Dong, You & Frangopol, Dan M., 2024. "An error-based stopping criterion for spherical decomposition-based adaptive Kriging model and rare event estimation," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

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