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Probabilistic Analysis of Slopes by Finite Element Method
Abstract A 2D deterministic stability analysis of the frictional slope is done in order to estimate the coefficient of security using the finite element limit analysis. The methodology makes use of the Optum G2 software. Hence, a 2D probabilistic study was considered by the finite element analysis with the random field theory according to the Monte Carlo simulation by the Karhunen–Loeve approach, the spatial variability and the local averaging of the soil’s random variables were addressed into the analysis to determine the effect of their random log-normal distribution in a parametric study, while considering the rest of the slope’s parameters as constants. Hence, the choice of the stochastic parameters of the slope (coefficient of variation COV, correlation lengths θ) is taken into account to evaluate the probability of failure. The analysis was repeated until obtaining stable statistics of the output utilizing 1000 Monte Carlo runs, and then representing the analysis results via the familiar software Microsoft Excel 2013. It is found that the mean value of the coefficient of security decreases with the raise of the spatial variability (COV), thus, it presents a reduction in the standard deviation. However, the probability of slope failure increases with COV for a corresponding high factor of security; and large values of the correlation lengths provide more smoothly varying field.
Probabilistic Analysis of Slopes by Finite Element Method
Abstract A 2D deterministic stability analysis of the frictional slope is done in order to estimate the coefficient of security using the finite element limit analysis. The methodology makes use of the Optum G2 software. Hence, a 2D probabilistic study was considered by the finite element analysis with the random field theory according to the Monte Carlo simulation by the Karhunen–Loeve approach, the spatial variability and the local averaging of the soil’s random variables were addressed into the analysis to determine the effect of their random log-normal distribution in a parametric study, while considering the rest of the slope’s parameters as constants. Hence, the choice of the stochastic parameters of the slope (coefficient of variation COV, correlation lengths θ) is taken into account to evaluate the probability of failure. The analysis was repeated until obtaining stable statistics of the output utilizing 1000 Monte Carlo runs, and then representing the analysis results via the familiar software Microsoft Excel 2013. It is found that the mean value of the coefficient of security decreases with the raise of the spatial variability (COV), thus, it presents a reduction in the standard deviation. However, the probability of slope failure increases with COV for a corresponding high factor of security; and large values of the correlation lengths provide more smoothly varying field.
Probabilistic Analysis of Slopes by Finite Element Method
Bougouffa, Imene (author) / Mellas, Mekki (author) / Baheddi, Mohamed (author)
2018-05-13
14 pages
Article/Chapter (Book)
Electronic Resource
English
Finite element , Monte Carlo simulation , Probability , Slope stability , Spatial variability Engineering , Civil Engineering , Geotechnical Engineering & Applied Earth Sciences , Remote Sensing/Photogrammetry , Hydrology/Water Resources , Climate Change/Climate Change Impacts , Image Processing and Computer Vision
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