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Research on progressive failure process of Baishuihe landslide based on Monte Carlo model

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Abstract

To the progressive landslide, development of the internal deformation and failure situation can’t be accurately reflected by the overall stability of coefficients and failure probability. But this problem can be solved by utilizing the principle of progressive failure by slices. Taking the warning area of Baishuihe landslide as an example, 5 days accumulated rainfall in different reappearing period is computed by Gumbel model. The failure probability of each slice is calculated by progressive failure principle, which is based on Monte Carlo model. The following results can be revealed through calculation: Overall stability and failure probability can’t reflect real situation of Baishuihe landslide warning area. Through building the calculation of progressive failure model of each slice, the stability of each part in the Baishuihe landslide warning area is quite different. Unstable region mainly lies in vicinity of the middle and posterior warning area. The front of the warning area remains stable. Deformation characteristics of the warning area are consistent with the investigation report. The scope of unstable area increased gradually with rainfall and the decline of reservoir water. Under 5 day’s accumulated rainfall of 50 years, the poor stable and unstable region reached 75 %, there is a large possibility of local deformation slip. Under the joint action of rainfall and reservoir water level, the warning area of Baishuihe landslide shows a progressive failure mode from top to bottom.

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Acknowledgments

This research is supported by the National Key Basic Research and Development Program of China (No. 2011CB710606) and the National Natural Science Foundation of China (No. 41272307 and No. 41572278). Thanks the colleagues in our laboratory for their constructive comments and assistance.

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Correspondence to Yiping Wu.

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Miao, F., Wu, Y., Xie, Y. et al. Research on progressive failure process of Baishuihe landslide based on Monte Carlo model. Stoch Environ Res Risk Assess 31, 1683–1696 (2017). https://doi.org/10.1007/s00477-016-1224-8

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  • DOI: https://doi.org/10.1007/s00477-016-1224-8

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