AbstractIn situ soil properties exhibit spatial variability, which is often described using a three-dimensional (3D) random field. With site investigations, soil properties at some specific locations are available. The corresponding data can be incorporated by a conditional random field to update the uncertainty parameters so that a more realistic or refined model can be achieved. Two algorithms, the Kriging and patching algorithms, are introduced for generating a 3D conditional random field. The conditional random field is linked with finite-element modeling, within the framework of Monte Carlo, to evaluate the performance of these two approaches in slope stability analyses. Sparsely distributed borehole data and cone penetration test (CPT) data are considered. The results indicate that for cases with limited sampled data, the patching algorithm gains an advantage over the Kriging algorithm in terms of prediction accuracy and uncertainty reduction. Data near the sliding surfaces of a slope remarkably affect the stability; thus, with sufficient ground information near the sliding surfaces, a conditional random field can provide better guidance for slope design.