Probabilistic identification of underground soil stratification using cone penetration tests

This paper develops Bayesian approaches for underground soil stratum identification and soil classification using cone penetration tests (CPTs). The uncertainty in the CPT-based soil classification using the Robertson chart is modeled explicitly in the Bayesian approaches, and the probability that the soil belongs to one of the nine soil types in the Robertson chart based on a set of CPT data is formulated using the maximum entropy principle. The proposed Bayesian approaches contain two major components: a Bayesian model class selection approach to identify the most probable number of underground soil layers and a Bayesian system identification approach to simultaneously estimate the most probable layer thicknesses and classify the soil types. Equations are derived for the Bayesian approaches, and the proposed approaches are illustrated using a real-life CPT performed at the National Geotechnical Experimentation Site (NGES) at Texas A&M University, USA. It has been shown that the proposed approaches properly identify the underground soil stratification and classify the soil type of each layer. In addition, as the number of model classes increases, the Bayesian model class selection approach identifies the soil layers progressively, starting from the statistically most significant boundary and gradually zooming into less significant ones with improved resolution. Furthermore, it is found that the evolution of the identified soil strata as the model class increases provides additional valuable information for assisting in the interpretation of CPT data in a rational and transparent manner.