Reliability-based analysis of cantilever retaining walls requires consideration of different failure mechanisms. In this paper, the reliability of soil-wall system is assessed considering two failure modes: rotational and structural stability, and the system reliability is assumed as a series system. The methodology is based on Monte Carlo Simulation (MCS), and it deals with the variability of the design parameters in the limit equilibrium analysis of a wall embedded in granular soil. Results of the MCS indicate that the reliability of the failure components increases exponentially by increasing the variability of design parameters. The results of the system reliability indicate how the system reliability is different from the component reliabilities. The strength of the weakest component influences the reliability of the system. The system reliability index increases with the wall section gradually. However it remains constant for the rotational failure mode.

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.

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