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A novel method for time-dependent small failure probability estimation of slope instability subjected to stochastic seismic excitations

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  • Li, Sihan
  • Wang, Xingliang
  • Pang, Rui
  • Xu, Bin

Abstract

The computational demand limits the estimation of small failure probabilities in geotechnical engineering under seismic excitation. This study proposes a novel method to estimate the time-dependent small failure probability of slope instability under non-stationary random seismic excitation. Initially, the kernel density estimation (KDE) with appropriate bandwidth is employed for preliminary estimation with the permanent displacement time history is utilized as the evaluation metric for the extreme value distribution (EVD). Subsequently, the EVD is refined using a two-step approach: a shifted generalized lognormal distribution (SGLD) models the main components, while a quadratic function models the tail, enabling the derivation of probability of exceedance (POE) curves on a logarithmic scale. The proposed method's effectiveness is verified through examples of soil and rock slopes subjected to non-stationary random seismic excitation, comparing direct KDE and Monte Carlo simulation (MCS). Results show that the method accurately estimates small failure probabilities of slope instability, has strong numerical stability and flexibility for various slope conditions.

Suggested Citation

  • Li, Sihan & Wang, Xingliang & Pang, Rui & Xu, Bin, 2025. "A novel method for time-dependent small failure probability estimation of slope instability subjected to stochastic seismic excitations," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
  • Handle: RePEc:eee:reensy:v:260:y:2025:i:c:s0951832025002339
    DOI: 10.1016/j.ress.2025.111032
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    References listed on IDEAS

    as
    1. Qian, Hua-Ming & Li, Yan-Feng & Huang, Hong-Zhong, 2021. "Time-variant system reliability analysis method for a small failure probability problem," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    2. Zhan, Hongyou & Xiao, Ning-Cong & Ji, Yuxiang, 2022. "An adaptive parallel learning dependent Kriging model for small failure probability problems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    3. Tingting Cheng & Jiti Gao & Xibin Zhang, 2019. "Nonparametric localized bandwidth selection for Kernel density estimation," Econometric Reviews, Taylor & Francis Journals, vol. 38(7), pages 733-762, August.
    4. Yu, Shui & Ren, Yuyao & Wu, Xiao & Guo, Peng & Li, Yun, 2024. "Dynamic pruning-based Bayesian support vector regression for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    5. Zhou, Jin & Li, Jie, 2022. "An enhanced method for improving the accuracy of small failure probability of structures," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    6. Cao, Runan & Sun, Zhili & Wang, Jian & Guo, Fanyi, 2022. "A single-loop reliability analysis strategy for time-dependent problems with small failure probability," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    7. Li, Guofa & Wang, Tianzhe & Chen, Zequan & He, Jialong & Wang, Xiaoye & Du, Xuejiao, 2023. "RBIK-SS: A parallel adaptive structural reliability analysis method for rare failure events," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    8. Chen, Zequan & Li, Guofa & He, Jialong & Yang, Zhaojun & Wang, Jili, 2022. "Adaptive structural reliability analysis method based on confidence interval squeezing," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    9. Hu, Xiaonong & Fang, Genshen & Yang, Jiayu & Zhao, Lin & Ge, Yaojun, 2023. "Simplified models for uncertainty quantification of extreme events using Monte Carlo technique," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    10. Zhao, Zhao & Lu, Zhao-Hui & Zhang, Xuan-Yi & Zhao, Yan-Gang, 2022. "A nested single-loop Kriging model coupled with subset simulation for time-dependent system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    11. Chen, Zequan & He, Jialong & Li, Guofa & Yang, Zhaojun & Wang, Tianzhe & Du, Xuejiao, 2024. "Fast convergence strategy for adaptive structural reliability analysis based on kriging believer criterion and importance sampling," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    12. Pepper, Nick & Crespo, Luis & Montomoli, Francesco, 2022. "Adaptive learning for reliability analysis using Support Vector Machines," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    13. Dasgupta, Agnimitra & Johnson, Erik A., 2024. "REIN: Reliability Estimation via Importance sampling with Normalizing flows," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    14. 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).
    15. Li, Wenxiong & Geng, Rong & Chen, Suiyin, 2024. "CSP-free adaptive Kriging surrogate model method for reliability analysis with small failure probability," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    16. Wang, Jinsheng & Xu, Guoji & Li, Yongle & Kareem, Ahsan, 2022. "AKSE: A novel adaptive Kriging method combining sampling region scheme and error-based stopping criterion for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    17. Chen, Weidong & Xu, Chunlong & Shi, Yaqin & Ma, Jingxin & Lu, Shengzhuo, 2019. "A hybrid Kriging-based reliability method for small failure probabilities," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 31-41.
    18. Dai, Baorui & Xia, Ye & Li, Qi, 2022. "An extreme value prediction method based on clustering algorithm," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    19. Razaaly, Nassim & Congedo, Pietro Marco, 2020. "Extension of AK-MCS for the efficient computation of very small failure probabilities," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    Full references (including those not matched with items on IDEAS)

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