Probabilistic Characterization of Undrained Shear Strength Using Liquidity Index

This paper develops Bayesian approaches for probabilistic characterization of the undrained shear strength S_u of clay using limited project-specific liquidity index (LI) data. The proposed Bayesian approaches include two major components: a Bayesian model comparison approach that selects a model for probabilistic characterization of S_u among several candidates and a Bayesian equivalent sample approach that employs Markov Chain Monte Carlo simulation (MCMCS) to generate a large number, as many as needed, of equivalent samples of the S_u profile based on both prior knowledge (e.g., engineering judgment/local experience) and project-specific LI data. Conventional statistical analysis of the equivalent samples is then carried out to estimate statistics (e.g., mean and standard deviation) of the S_u profile. The proposed approaches are illustrated and validated using the LI data simulated from a virtual clay site in a series of sensitivity studies. The results show that the proposed approaches properly identify the correct model for probabilistic characterization of S_u and provide reasonable statistics of the S_u profile based on prior knowledge and limited project-specific LI data. The accuracy of the estimated statistics improves as the number of test data used in the proposed approaches increases.