Efficient slope reliability analysis based on representative slip surfaces: a comparative study - Zhu, Wen-Qing; Zhang, Shao-He; Li, Yue-Hua; Liu, Jian (Frontiers in Earth Science, 2023)
Abstract: Slope reliability analysis can be alternatively conducted based on representative slip surfaces (RSSs), which is more efficient than the conventional analysis based on a great many potential slip surfaces (PSSs). Various methods for selection of RSSs are, therefore, frequently proposed to enhance the efficiency of slope reliability analysis. These methods, however, generally require a complex calculation procedure (e.g., evaluation of reliability index for each PSS and/or correlation coefficients among PSSs) that cannot adaptively single out the RSSs; and the selected RSSs by these methods are commonly related to the statistics of soil properties. This leads to the question of how to identify efficiently and adaptively the RSSs of a slope for a subsequent reliability analysis with many parametric studies. To answer this question, an adaptive K-means clustering-based RSSs (AKCBR) selection method has been recently developed, which is able to select the RSSs adaptively and efficiently from many PSSs. And the RSSs identified by AKCBR do not vary with the variation of soil statistics, such as the inherent spatial variability which greatly is beneficial to slope reliability analysis involving many parametric studies. As such, limitations of the available methods are tackled in AKCBR. A comprehensive comparative study is conducted in this paper to explore in detail the strength and weaknesses of the AKCBR against the available methods. Four slope examples that represent four kinds of slope stability problems are considered. Results show that AKCBR provides comparable reliability results with the available methods, in terms of probability of failure and the most dominant failure modes, but it is generally more efficient. The AKCBR can adaptively identify the RSSs of slopes belonging to different type, and the RSSs are statistically robust against the statistics of soil properties, which is beneficial to reliability analysis involving many parametric studies.