This paper presents a new method to analyze the slope stability that considers the spatial variability of soil properties from the probabilistic point of view. Orthogonal decomposition is performed to spatial covariance matrix of soil strength by Karhunen-Loeve expansion (K-L). Then the Latin Hypercube Sampling (LHS) and the Cholesky decomposition method are used to optimize the generated spatial random fields. In the proposed method, a readily available concise algorithm is utilized to calculate the factor of safety for each preset slip surface. The optimization technique is then adopted to search for the global critical slip surface. The generated samples are examined to be rational for they are good enough to match the objective function. Results show that the samples can reduce the simulation number greatly compared to Monte Carlo method (MC), while the calculated failure probabilities from LHS method and MC method are close to each other. Sensitivity analysis of geometric parameters shows that the correlation length exerts minor influence on the slope stability. The failure probability increases with the increasing of slope angle, slope height and the coefficient of variability. For a given slope, the results show that only the combined using of factor of safety and failure probability could be effective enough for the slope safety evaluation.
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