Probabilistic soil classification and stratification in a vertical cross-section from limited cone penetration tests using random field and Monte Carlo simulation

Classification and stratification (or zonation) of subsurface soils are important tasks in geotechnical site investigation. Due to the limit of time, budget, or access to subsurface soils, subsurface soil information obtained from investigation points (e.g., boreholes, cone penetration tests (CPTs)) in a specific site is often limited (e.g., a few boreholes or CPT soundings), resulting in great challenge in interpretation of the site investigation data obtained and significant uncertainty in the inferred subsurface soil classification and stratification. A novel probabilistic method is developed in this paper for properly accounting for the uncertainty associated with CPT-based subsurface soil classification and stratification. The method properly classifies and stratifies subsurface soils in a 2D vertical cross-section from limited CPT soundings. A limited number of 1D CPT sounding data is firstly interpolated to produce a 2D vertical cross-section, and the associated interpolation uncertainty is modelled explicitly using random field theory. Probabilistic soil classification model is also developed to account for the uncertainty associated with the empirical soil behavior type classification model. Then, the interpolation uncertainty and soil classification model uncertainty are considered simultaneously in a Monte Carlo simulation framework. Both simulated and real data examples are used to illustrate the proposed method. The results indicate that the proposed method well predicts subsurface soil classification and stratification in a 2D vertical cross-section from limited CPT soundings, and properly quantifies the associated uncertainty. In addition, sensitivity studies on interpolation uncertainty and soil classification model uncertainty are performed.