Title: Bayesian inverse analysis for geotechnical site characterisation using cone penetration test

Authors: Zijun Cao; Kai Huang; Yu Wang

Addresses: State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, 8 Donghu South Road, Wuhan, China; State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu, China ' Department of Architecture and Civil Engineering, Shenzhen Research Institute, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong ' Department of Architecture and Civil Engineering, Shenzhen Research Institute, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong

Abstract: 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.

Keywords: geotechnical site characterisation; inverse analysis; Bayesian system identification; Bayesian model class selection; soil stratification; cone penetration test; underground stratigraphy; soil properties; uncertainty; soil classification; soil type.

DOI: 10.1504/IJRS.2014.069509

International Journal of Reliability and Safety, 2014 Vol.8 No.2/3/4, pp.97 - 116

Received: 14 Mar 2014
Accepted: 30 Sep 2014
Published online: 20 May 2015 *

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