Bayesian inverse analysis for geotechnical site characterisation using cone penetration test

Extracting information on underground stratigraphy (i.e. the number of soil layers and their thicknesses underground) and soil properties from in-situ and/or laboratory test results [e.g. Cone Penetration Test (CPT) data] is an elementary step in geotechnical analysis and design. This task can be treated as an inverse analysis problem. This paper develops a Bayesian inverse analysis approach for the interpretation of CPT data, which makes use of CPT data as input of the inverse analysis and identifies the underground stratigraphy and the soil type in each soil layer. It is integrated with the Robertson chart to explicitly and properly consider the uncertainty in the CPT-based soil classification and the spatial distribution of the CPT data. The proposed approach is illustrated and verified using real-life and simulated CPT data. It is shown that the proposed approach properly identifies the underground soil stratification and classifies the soil type of each layer.