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METHOD TO ESTIMATE THE SYSTEM PROBABILITY OF FAILURE FOR SLOPE STABILITY ANALYSIS
Author(s) -
Chollada Kanjanakul
Publication year - 2018
Publication title -
international journal of geomate
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 17
eISSN - 2186-2990
pISSN - 2186-2982
DOI - 10.21660/2018.45.gte57
Subject(s) - stability (learning theory) , slope stability , slope stability analysis , reliability engineering , mathematics , statistics , computer science , geotechnical engineering , engineering , machine learning
Uncertainty in soil slope is one of the problems in slope stability analysis because natural soil slope is heterogeneous that is difficult to predict time and location of the failure. This problem leads that only conventional approaches do not take into account many risks related to slope safety. The optimization of this problem is carried out using probabilistic slope stability analysis. This paper described a method to calculate the probability of failure of slope stability analysis. The system probability of failure is defined as the complement of the sum of the probability of failure corresponding to sliding failure. Furthermore, this paper shows time-steps in the developments of factors of safety in soil slope and perform natural slope stability analyses by probabilistic approach on a hill range. Sensitivity result indicated that the effective angle of internal friction (φ) and the angle of soil slope (β) are most significant parameters and choose to be random variable parameters. Probabilistic for soil slope failure is calculated using conventional design equations, mean and coefficient of variation values for the random variable parameters as input. GeoStudio’s program (SEEP/W and SLOPE/W application) have been employed to describe the process of rainfall infiltration under positive and negative pore-water pressures and slope stability analyses, respectively. Bishop’s simplified method was used in conjunction with Monte Carlo Simulation to determine in term of the probability of failure (Pf). This approach is to prove the best confidence result in slope stability analysis.

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