4.6 Article

Influence of Rainfall Pattern on Wetness Index for Infinite Slope Stability Analysis

期刊

WATER
卷 15, 期 14, 页码 -

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MDPI
DOI: 10.3390/w15142535

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factor of safety; landslide; rainfall pattern; slope stability; wetness index

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This study focuses on evaluating the influence of different rainfall patterns on slope stability and compares it with an actual landslide incident that occurred in Korea in 2011. The study found that models considering abrupt rainfall intensity can more accurately capture the timing of landslide occurrences. Therefore, the appropriate adoption of a rainfall distribution model should be highlighted for landslide prediction.
Landslides are one of the riskiest disasters combining excessive rainfall and unstable slope that a wetness index can quantify. The wetness index generated by water infiltration considering the rainfall pattern such as cumulated rainfall, rainfall duration and rainfall intensity should be estimated for the slope stability analysis. Even though the infiltration capacity of soils has been largely focused to evaluate the slope stability, the temporal patterns of rainfall have commonly been ignored or assumed as a steady state for the prediction of the slope failure in the previous studies. Thus, this study focuses more on evaluating the influence of various rainfall patterns on the slope stability, and compares it with an actual landslide incident that occurred in 2011, in Korea. The factor of safety (FS) considering the time-dependent wetness index variation is used to determine the slope stability. For the various rainfall designs, the uniform rainfall distribution, Yen and Chow, Mononobe, alternating block and second quartile Huff models are adopted. Thereafter, the FS variations from five models are compared with an actual landslide incident in Seoul, Korea. Among the rainfall designs, the models that consider the abrupt rainfall intensity capture the landslide time with an FS < 1. Therefore, the appropriate adoption of a rainfall distribution model should be highlighted for landslide prediction.

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