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Risk de-aggregation and system reliability analysis of slope stability using representative slip surfaces

机译:使用代表性滑移面的边坡稳定性风险分解和系统可靠性分析

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摘要

This paper develops a risk de-aggregation and system reliability approach to evaluate the slope failure probability, p_f, using representative slip surfaces together with MCS. An efficient procedure is developed to strategically select the candidate representative slip surfaces, and a risk de-aggregation approach is proposed to quantify contribution of each candidate representative slip surface to the p_f, identify the representative slip surfaces, and determine how many representative slip surfaces are needed for estimating the p_f with reasonable accuracy. Risk de-aggregation is performed by collecting the failure samples generated in MCS and analyzing them statistically. The proposed methodology is illustrated through a cohesive soil slope example and validated against results from previous studies. When compared with the previous studies, the proposed approach substantially improves the computational efficiency in probabilistic slope stability analysis. The proposed approach is used to explore the effect of spatial variability on the p_f. It is found that, when spatial variability is ignored or perfect correlation assumed, the p_f of the whole slope system can be solely attributed to a single representative slip surface. In this case, it is theoretically appropriate to use only one slip surface in the reliability analysis. As the spatial variability becomes growingly significant, the number of representative slip surfaces increases, and all representative slip surfaces (i.e., failure modes) contribute more equally to the overall system risk. The variation of failure modes has substantial effect on the p_f, and all representative surfaces have to be incorporated properly in the reliability analysis. The risk de-aggregation and system reliability approach developed in this paper provides a practical and efficient means to incorporate such a variation of failure modes in probabilistic slope stability analysis.
机译:本文开发了一种风险分解和系统可靠性方法,使用代表性的滑动面和MCS来评估边坡破坏概率p_f。开发了一种有效的方法来从战略上选择候选代表性滑动面,并提出了一种风险分解方法来量化每个候选代表性滑动面对p_f的贡献,识别代表性滑动面并确定有多少代表性滑动面。以合理的精度估算p_f所需的。通过收集在MCS中生成的故障样本并进行统计分析,可以进行风险分解。通过粘性土坡示例说明了所提出的方法,并根据先前研究的结果进行了验证。与以前的研究相比,该方法大大提高了概率边坡稳定性分析的计算效率。所提出的方法用于探索空间变异性对p_f的影响。发现,当忽略空间变异性或假设完全相关时,整个坡度系统的p_f可以仅归因于单个代表性滑动面。在这种情况下,从理论上讲,在可靠性分析中仅使用一个滑动面是合适的。随着空间可变性变得越来越重要,代表性滑动面的数量增加,并且所有代表性滑动面(即,失效模式)对整个系统风险的贡献更大。失效模式的变化对p_f有很大影响,并且所有代表性表面都必须在可靠性分析中适当纳入。本文开发的风险分解和系统可靠性方法提供了一种实用有效的方法,可以将这种破坏模式的变化纳入概率边坡稳定性分析中。

著录项

  • 来源
    《Computers and Geotechnics》 |2013年第9期|95-105|共11页
  • 作者单位

    School of Civil Engineering Qingdao Technological University, Qingdao, PR China;

    Department of Civil and Architectural Engineering, Shenzhen Research Institute, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong;

    Department of Civil and Architectural Engineering, Shenzhen Research Institute, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong;

    School of Civil Engineering Qingdao Technological University, Qingdao, PR China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Probabilistic slope stability analysis; Monte Carlo simulation; Statistical analysis; Failure samples;

    机译:概率边坡稳定性分析;蒙特卡罗模拟;统计分析;失败样本;

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