Probabilistic Analysis of Infinite Slope Stability Considering Variation in Soil Depth
Source abstract
Abstract
In probabilistic slope stability analysis, soil depth has been treated as a deterministic variable, although it is a highly variable parameter. This study aims to identify soil depth variability using seismic refraction survey data and to analyze its impact on probabilistic analysis of slope stability. Seismic refraction survey data were collected from 70 slopes in South Korea and employed to identify the variability of soil depth within natural slopes. As a result, the average soil depth across 70 slopes was 2.5 m, with an average coefficient of variation (COV) of 29%, indicating high variability. To investigate the influence of soil depth variability on the probability of slope failure, probabilistic slope stability analysis was conducted by considering the shear strength parameters of soil and soil depth as random variables. Accordingly, the influences of the variability of soil depth on the probabilistic analysis of slope stability were evaluated by comparing the probability of slope failure and distribution of the failure occurrence frequency by depth. Additionally, global sensitivity analysis was conducted to understand the relative contribution of input parameters on the probability of slope failure. Consequently, the probability of slope failure can vary significantly depending on soil depth variability, emphasizing the importance of considering this factor in probabilistic slope stability analysis.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Geotechnical Engineering and Analysis · Landslides and related hazards · Soil and Unsaturated Flow
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Forest Engineering, Resources and Management, Oregon State University, Corvallis, OR 97331, USA
Department of Forest Science, Chungbuk National University, Cheongju 28644, Republic of Korea
Department of Agricultural and Rural Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea
Funding
National Research Foundation of Korea
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