Reliability analysis of slopes considering spatial variability of soil properties based on efficiently identified representative slip surfaces
Reliability analysis of slopes considering spatial variability of soil properties based on efficiently identified representative slip surfaces
复制标题
基于有效识别的代表性滑动面考虑土壤特性空间变异性的边坡可靠性分析
DOI：
10.1016/j.jrmge.2019.12.003
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发表时间：
2020
影响因子：
7.3
通讯作者：
Quan Jiang
中科院分区：
文献类型：
--
作者：
Bin Wang;Leilei Liu;Yuehua Li;Quan Jiang
当使用响应面法（RSM）时，由于“维数灾难”，考虑土壤性质固有空间变异性（ISV）的边坡可靠性分析非常耗时。本文提出了一种在极限平衡法 (LEM) 框架内利用自适应 K 均值聚类方法识别具有空间变化土壤的斜坡的代表性滑移面 (RSS) 的有效方法。然后，从计算效率的角度，建立了考虑土壤空间变异性的基于RSS和RSM的改进边坡可靠度分析。该方法的详细实施过程已有详细记录，并且通过三个斜率示例研究了该方法识别 RSS 和估计可靠性的能力。结果表明，该方法只需对常规确定性边坡稳定性模型进行一次评估即可自动识别边坡的RSS。 RSS 与土壤特性的统计数据保持不变，这使得边坡可靠性分析中经常需要的参数研究可以轻松有效地实现。研究还发现，该方法提供的边坡安全系数（FS）和破坏概率（Pf）值与直接分析和文献中获得的值相当。
Slope reliability analysis considering inherent spatial variability (ISV) of soil properties is time-consuming when response surface method (RSM) is used, because of the “curse of dimensionality”. This paper proposes an effective method for identification of representative slip surfaces (RSSs) of slopes with spatially varied soils within the framework of limit equilibrium method (LEM), which utilizes an adaptiveK-means clustering approach. Then, an improved slope reliability analysis based on the RSSs and RSM considering soil spatial variability, in perspective of computation efficiency, is established. The detailed implementation procedure of the proposed method is well documented, and the ability of the method in identifying RSSs and estimating reliability is investigated via three slope examples. Results show that the proposed method can automatically identify the RSSs of slope with only one evaluation of the conventional deterministic slope stability model. The RSSs are invariant with the statistics of soil properties, which allows parametric studies that are often required in slope reliability analysis to be efficiently achieved with ease. It is also found that the proposed method provides comparable values of factor of safety (FS) and probability of failure (Pf) of slopes with those obtained from direct analysis and literature.