IDENTIFICATION OF SUBSURFACE SOIL STRATIFICATION USING CONE PENETRATION TESTS AND BAYESIAN LEARNING

Abstract

Cone penetration test(CPT) is usually performed vertically to identify subsurface soil stratification. However,due to time and budget constraints,the number of CPT soundings performed in a site is often limited,leading to a great challenge in properly interpreting CPT data and identifying stratification in unsounded area along horizontal direction. A Bayesian learning method is presented in this paper to address this difficulty. The method can predict soil classification and stratification in a two-dimensional(2D)vertical cross-section using a limited number of CPT soundings. The method consists of three components:(1)2D interpolation of CPT data using Bayesian learning; (2)determination of soil behavior type(SBT)using Robertson chart at every location in the 2D cross-section,including locations with and without CPT soundings; (3) and soil layer/zone delineation using an edge detection method. High-resolution CPT data and SBT information in the 2D vertical cross-section can be obtained. Soil layer/zone boundaries are delineated automatically. The method is illustrated using a simulated example. The results suggest that the method performs well even when only five sets of CPT soundings are available.