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IE-AK: A novel adaptive sampling strategy based on information entropy for Kriging in metamodel-based reliability analysis

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  • Zhou, Jin
  • Li, Jie

Abstract

This article focuses on the adaptive Kriging metamodel-based reliability analysis for reducing a sequential number of calls of the complex original functions. To avoid the repetitive and tedious deterministic response analysis with stochastic simulation method (including Monte Carlo Simulation and its various improvement, such as importance sampling, subset simulation) in reliability analysis, herein a novel sequential sampling strategy related to Kriging metamodel is proposed, which is implemented based on information entropy theory. In addition, the generalized F-discrepancy method is simultaneously quoted to further optimize the candidate pool to improve the effectiveness of the training metamodel. Finally, a new structural reliability analysis method is proposed, which continuously reduces the number of deterministic analysis of structures without sacrificing accuracy. To highlight the applicability of the method and verify its accuracy and effectiveness, a series of typical examples are tested and compared, including highly nonlinear limit state functions, high-dimension performance function with analytic expressions and dynamic reliability analysis of nonlinear engineering structures subject to seismic excitation with implicit performance function. Numerical results show that significant computational savings and desired accuracy can be achieved when dealing with different reliability analysis cases.

Suggested Citation

  • Zhou, Jin & Li, Jie, 2023. "IE-AK: A novel adaptive sampling strategy based on information entropy for Kriging in metamodel-based reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    • Handle: RePEc:eee:reensy:v:229:y:2023:i:c:s0951832022004434
    DOI: 10.1016/j.ress.2022.108824
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    References listed on IDEAS

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    1. Sun, Zhili & Wang, Jian & Li, Rui & Tong, Cao, 2017. "LIF: A new Kriging based learning function and its application to structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 152-165.
    2. Zhang, Xufang & Wang, Lei & Sørensen, John Dalsgaard, 2019. "REIF: A novel active-learning function toward adaptive Kriging surrogate models for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 440-454.
    3. Shi, Yan & Lu, Zhenzhou & He, Ruyang & Zhou, Yicheng & Chen, Siyu, 2020. "A novel learning function based on Kriging for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
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    5. Cadini, F. & Santos, F. & Zio, E., 2014. "An improved adaptive kriging-based importance technique for sampling multiple failure regions of low probability," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 109-117.
    6. Zhang, Jinhao & Xiao, Mi & Gao, Liang, 2019. "An active learning reliability method combining Kriging constructed with exploration and exploitation of failure region and subset simulation," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 90-102.
    7. Zhou, Tong & Peng, Yongbo, 2022. "Reliability analysis using adaptive Polynomial-Chaos Kriging and probability density evolution method," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    8. Roy, Atin & Chakraborty, Subrata, 2020. "Support vector regression based metamodel by sequential adaptive sampling for reliability analysis of structures," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    9. Wang, Jian & Sun, Zhili & Cao, Runan, 2021. "An efficient and robust Kriging-based method for system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    10. Wen, Zhixun & Pei, Haiqing & Liu, Hai & Yue, Zhufeng, 2016. "A Sequential Kriging reliability analysis method with characteristics of adaptive sampling regions and parallelizability," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 170-179.
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    Cited by:

    1. Li, Jin-Yang & Lu, Jubin & Zhou, Hao, 2023. "Reliability analysis of structures with inerter-based isolation layer under stochastic seismic excitations," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    2. Ding, Jiayi & Zhou, Jianfang & Cai, Wei, 2023. "An efficient variable selection-based Kriging model method for the reliability analysis of slopes with spatially variable soils," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    3. Luo, Changqi & Zhu, Shun-Peng & Keshtegar, Behrooz & Niu, Xiaopeng & Taylan, Osman, 2023. "An enhanced uniform simulation approach coupled with SVR for efficient structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    4. Wang, Lei & Hu, Zhuo & Dang, Chao & Beer, Michael, 2024. "Refined parallel adaptive Bayesian quadrature for estimating small failure probabilities," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    5. Bakeer, Tammam, 2023. "General partial safety factor theory for the assessment of the reliability of nonlinear structural systems," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    6. Bao, Yuequan & Sun, Huabin & Guan, Xiaoshu & Tian, Yuxuan, 2024. "An active learning method using deep adversarial autoencoder-based sufficient dimension reduction neural network for high-dimensional reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    7. Yuan, Kai & Sui, Xi & Zhang, Shijie & Xiao, Ning-cong & Hu, Jinghan, 2024. "AK-SYS-IE: A novel adaptive Kriging-based method for system reliability assessment combining information entropy," Reliability Engineering and System Safety, Elsevier, vol. 246(C).

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