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A new rating system approach for risk analysis of rock slopes

Author

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  • Reza Nassirzadeh Goorchi

    (University of Tehran)

  • Mehdi Amini

    (University of Tehran)

  • Hossein Memarian

    (University of Tehran)

Abstract

In this paper, an approach is presented to analyze the stability risk of rock slopes based on a new rating system. Three factors are used to estimate the risk level of rock slopes: (1) failure probability, (2) element at risk rating, and (3) vulnerability rating. Element at risk and vulnerability ratings are both given a range from 0 to 10, and the probability of failure is varied between 0 and 1, so the risk rating ranges between 0 and 100. This risk rating can be used to determine both the quantitative and qualitative risk levels of slopes at the same time. The method is tested on the western sector of the slopes facing Songun copper plant phase III, Iran, to clarify its procedures and assess its validity. Deterministic kinematic analyses showed that the slope has a potential for circular failure. Risk assessments revealed that the risk levels of the slope in both static and pseudo-static conditions are “very low” and “high,” respectively.

Suggested Citation

  • Reza Nassirzadeh Goorchi & Mehdi Amini & Hossein Memarian, 2018. "A new rating system approach for risk analysis of rock slopes," 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. 91(1), pages 75-102, April.
    • Handle: RePEc:spr:nathaz:v:91:y:2018:i:1:d:10.1007_s11069-017-3144-4
    DOI: 10.1007/s11069-017-3144-4
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    References listed on IDEAS

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    1. Claudia Mignelli & Stefano Russo & Daniele Peila, 2012. "ROckfall risk MAnagement assessment: the RO.MA. approach," 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. 62(3), pages 1109-1123, July.
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    Cited by:

    1. Cuiying Zhou & Jinwu Ouyang & Zhen Liu & Lihai Zhang, 2022. "Early Risk Warning of Highway Soft Rock Slope Group Using Fuzzy-Based Machine Learning," Sustainability, MDPI, vol. 14(6), pages 1-28, March.

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