Soil Probabilistic Slope Stability Analysis Using Stochastic Finite Difference Method

Abstract

The paper contrasts results obtained by the partially factored limit state design method and a more advanced Random Finite Difference Method (RFDM) in a benchmark problem of slope stability analysis with variable undrained shear strength. Local Average Subdivision method was used to simulate the non-Gaussian random variables. The key difference between the methods is that RFDM takes into account spatial variability of soil parameters allowing slope failure to occur naturally along the path of least resistance. The probabilistic method leads to predictions of the "probability of slope failure" as opposed to the more traditional "factor of safety" measure of slope safety in the limit state design method; however, they give significant different results depending on the level of the variability. Analyses conducted using Monte Carlo Simulation show that the same partial factor can have very different levels of risk depending on the degree of uncertainty of the mean value of the soil shear strength. Calibration studies show the partial factor necessary to achieve target probability values.

Multi-objective robust decision-making for optimizing Subway Station Support Systems under spatial variability of soil parameters

“…In contrast, the conditional random fields (CRF) method incorporates the spatial effects of borehole locations through geostatistical methods. While RFEM has found applications in geotechnical applications (see, for example, [11][12][13][14][15][16][17][18]), there exists limited research regarding the integration of FEM or FDM with CRF theory for the analysis and design of tunnel stability. This approach has been more extensively employed in slope stability analysis [19,20].…”

Estimating the Slope Safety Factor Using Simple Kinematically Admissible Solutions

“…Numerous other computational methods can be found in the literature and engineering practice. In addition to the traditional approaches discussed in [5], such as the Fellenius method, Janbu, or Sarma, more advanced computational approaches can be considered, for example, those based on probabilistic methods [14][15][16][17][18][19]; semi-probabilistic methods [20], including the stochastic finite difference method [21]; and those based on the finite element method [10,11,22].…”