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Bootstrap method for characterizing the effect of uncertainty in shear strength parameters on slope reliability

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  • Li, Dian-Qing
  • Tang, Xiao-Song
  • Phoon, Kok-Kwang

Abstract

This paper aims to propose a bootstrap method for characterizing the effect of uncertainty in shear strength parameters on slope reliability. The procedure for a traditional slope reliability analysis with fixed distributions of shear strength parameters is presented first. Then, the variations of the mean and standard deviation of shear strength parameters and the Akaike Information Criterion values associated with various distributions are studied to characterize the uncertainties in distribution parameters and types of shear strength parameters. The reliability of an infinite slope is presented to demonstrate the validity of the proposed method. The results indicate that the bootstrap method can effectively model the uncertain probability distributions of shear strength parameters. The uncertain distributions of shear strength parameters have a significant influence on slope reliability. With the bootstrap method, the slope reliability index is represented by a confidence interval instead of a single fixed index. The confidence interval increases with increasing factor of slope safety. Considering both the uncertainties in distribution parameters and distribution types of shear strength parameters leads to a higher variation and a wider confidence interval of reliability index.

Suggested Citation

  • Li, Dian-Qing & Tang, Xiao-Song & Phoon, Kok-Kwang, 2015. "Bootstrap method for characterizing the effect of uncertainty in shear strength parameters on slope reliability," Reliability Engineering and System Safety, Elsevier, vol. 140(C), pages 99-106.
    • Handle: RePEc:eee:reensy:v:140:y:2015:i:c:p:99-106
    DOI: 10.1016/j.ress.2015.03.034
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    References listed on IDEAS

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    1. Sentz, Kari & Ferson, Scott, 2011. "Probabilistic bounding analysis in the Quantification of Margins and Uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 96(9), pages 1126-1136.
    2. Huard, David & Evin, Guillaume & Favre, Anne-Catherine, 2006. "Bayesian copula selection," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 809-822, November.
    3. Wai Mun Fong, 2013. "Time diversification under loss aversion: a bootstrap analysis," Applied Economics, Taylor & Francis Journals, vol. 45(5), pages 605-610, February.
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    Cited by:

    1. Zhang, Ruijing & Dai, Hongzhe, 2022. "A non-Gaussian stochastic model from limited observations using polynomial chaos and fractional moments," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    2. Tonghui Wei & Wenjie Zuo & Hongwei Zheng & Feng Li, 2021. "Slope hybrid reliability analysis considering the uncertainty of probability-interval using three-parameter Weibull distribution," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(1), pages 565-586, January.
    3. 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).
    4. Lan, Meng & Zhu, Jiping & Lo, Siuming, 2021. "Hybrid Bayesian network-based landslide risk assessment method for modeling risk for industrial facilities subjected to landslides," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    5. 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).
    6. Zhao, Tengyuan & Wang, Yu, 2020. "Non-parametric simulation of non-stationary non-gaussian 3D random field samples directly from sparse measurements using signal decomposition and Markov Chain Monte Carlo (MCMC) simulation," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    7. Liu, Fuchao & Wei, Pengfei & Tang, Chenghu & Wang, Pan & Yue, Zhufeng, 2019. "Global sensitivity analysis for multivariate outputs based on multiple response Gaussian process model," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 287-298.

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