IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v105y2021i1d10.1007_s11069-020-04323-y.html
   My bibliography  Save this article

Slope hybrid reliability analysis considering the uncertainty of probability-interval using three-parameter Weibull distribution

Author

Listed:
  • Tonghui Wei

    (Jilin University)

  • Wenjie Zuo

    (Jilin University)

  • Hongwei Zheng

    (Jilin University)

  • Feng Li

    (Jilin University)

Abstract

A reliability model is proposed to solve the problem of hybrid uncertainty with both random and interval variables in slope engineering. A hybrid uncertainty model based on the dimension reduction method and Taylor expansion is constructed to approximate the limit state function. Using the polynomial theorem and variable transformation method, the origin and center moments’ interval of the limit state function are calculated. Moment information is applied to the expansion of a three-parameter Weibull distribution, and the cumulative distribution function and probability density function of limit state function are determined. As a result, the failure probability interval of the slope is calculated. The interval uncertainty problem is transformed into an interval certainty problem using Taylor expansion without solving for the statistical moment of limit state function using multiple integrals and iteratively searching for the most probable failure points. The numerical results from two slopes show that the proposed method is effective and feasible.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:nathaz:v:105:y:2021:i:1:d:10.1007_s11069-020-04323-y
    DOI: 10.1007/s11069-020-04323-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-020-04323-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-020-04323-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Andriy Andreev & Antti Kanto & Pekka Malo, 2007. "Computational Examples of a New Method for Distribution Selection in the Pearson System," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(4), pages 487-506.
    2. 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.
    3. Omer F. Usluogullari & Ahmet Temugan & Esra S. Duman, 2016. "Comparison of slope stabilization methods by three-dimensional finite element analysis," 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. 81(2), pages 1027-1050, March.
    4. Omer Usluogullari & Ahmet Temugan & Esra Duman, 2016. "Comparison of slope stabilization methods by three-dimensional finite element analysis," 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. 81(2), pages 1027-1050, March.
    5. Shaojun Li & Hong-Bo Zhao & Zhongliang Ru, 2013. "Slope reliability analysis by updated support vector machine and Monte Carlo simulation," 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. 65(1), pages 707-722, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yuke Wang & Musen Han, 2022. "Optimal design of slope reinforcement by a new developed polymer micro anti-slide pile in case of emergency and disaster relief," 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. 112(1), pages 899-917, May.
    2. Zhao Li & Da Huang, 2023. "Stability analysis of a water-rich slope stabilized by a novel upper-hollow drainage anti-slide pile," 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. 117(1), pages 425-446, May.
    3. Qiang Xie & Zhilin Cao & Renjun Tian & Weichen Sun & Alessio Fumagalli & Haiyou Peng & Xiang Fu & Haoyang Luo, 2024. "Complex sliding characteristics of landslides and evaluation of the reinforcement with arched anti-slide piles based on 3D discrete element method: a case study," 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. 120(9), pages 8983-9007, July.
    4. Huayang Lei & Xu Liu & Yingjie Song & Yinggang Xu, 2021. "Stability analysis of slope reinforced by double-row stabilizing piles with different locations," 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. 106(1), pages 19-42, March.
    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. 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).
    7. 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).
    8. Benjamin R. Auer, 2022. "On false discoveries of standard t-tests in investment management applications," Review of Managerial Science, Springer, vol. 16(3), pages 751-768, April.
    9. Wu, Xuedong & Chang, Yanchao & Mao, Jianxu & Du, Zhaoping, 2013. "Predicting reliability and failures of engine systems by single multiplicative neuron model with iterated nonlinear filters," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 244-250.
    10. 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).
    11. 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).
    12. 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.
    13. Arsalan Mahmoodzadeh & Mokhtar Mohammadi & Hunar Farid Hama Ali & Hawkar Hashim Ibrahim & Sazan Nariman Abdulhamid & Hamid Reza Nejati, 2022. "Prediction of safety factors for slope stability: comparison of machine learning techniques," 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. 111(2), pages 1771-1799, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:105:y:2021:i:1:d:10.1007_s11069-020-04323-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.