Probabilistic stability assessment of slope considering soft soil and silty clay foundations

Although deterministic analysis can provide initial insights into slope stability, there is no way to reflect the true distribution of soil properties within a slope. To further investigate the effects of the spatial variability of soil properties on the stability and failure mechanism of slope under different foundation types, this study develops a framework combining simple limit equilibrium method (LEM), Monte Carlo Simulation (MCS), and random field to incorporate these factors into slope probabilistic stability analysis. The slope models of two typical foundations (e.g., soft soil and silty clay foundations) are used to illustrate the proposed methodology. The results show that the shape of critical failure slip surface in slope with a soft foundation is deep and passes through the soft soil layer, indicating that the slope with a soft foundation has a variable failure mechanism. Due to the presence of soft soil foundation, the correlation between different critical failure slip surfaces is low and deterministic analysis may significantly overestimate slope stability. When performing a probabilistic stability analysis of the slope under the silty clay foundation, it is noted that the shape of critical failure slip surface is relatively shallow and the correlation between different critical failure slip surfaces is high. The degree of slope stability overestimation caused by deterministic analysis is significantly reduced. Note that when the factor of safety is used to evaluate slope stability, deterministic analysis can only evaluate slope stability more reasonably when the correlation of failure slip surface is high, while probabilistic method can evaluate slope stability more comprehensively and reasonably. The sensitivity analysis results show that the sensitivity of internal friction angle to slope stability is much higher than that of cohesive under different scales of fluctuation, coefficients of variation, and cross correlation coefficients.