Integrating Multi-Source Information from Site Investigation for Probabilistic Characterization of Undrained Shear Strength

This paper presents a Bayesian sequential updating (BSU) approach that integrates multi-source information obtained from geotechnical site investigation for probabilistic characterization of undrained shear strength su of clay. Herein, the multi-source information includes the knowledge available prior to the project (namely prior knowledge) and test results from various testing procedures, such as overconsolidation ratio (OCR), standard penetration test (SPT), and cone penetration test (CPT) data. In this study, the OCR, SPT, and CPT data are sequentially incorporated into a BSU framework to update the knowledge on su for determination of its site-specific statistics and probability distributions. The BSU framework allows using multiple types of test results from different test procedures at different locations. The proposed approach is illustrated and validated using OCR, SPT and CPT data simulated from a virtual clay site, where true statistics and probability distributions of su are known. Results showed that the proposed BSU approach combines prior knowledge with multiple types of test results in a consistent and systematic manner, and it provides reasonable statistical estimates of geotechnical parameters based on the combined information. In addition, effects of data quality and quantity are also explored using simulated data.