Bayesian Updating of a Spatially Varied Soil Property for Enhancing Reliability in Drilled Shaft Design

In this paper, a procedure for updating the spatially varied soil property of drilled shafts using Bayesian theorem is presented. The focus herein is to update the blow count of standard penetration test (SPT-N) using the observed ultimate resistance of drilled shafts in the field. The spatial variability of SPT-N in terms of scale of fluctuation is interpreted with the SPT-N profile obtained from prior site investigation. The updated SPT-N is expressed as posterior distribution, which is a function of the observed ultimate resistance and the prior SPT-N data. The Markov-chain-Monte-Carlo (MCMC) simulation-based sampling method is adopted to generate the posterior distributions of SPT-N. In this process, the correlation of the spatial averages of SPT-N values for estimating shaft resistance and toe bearing resistance is explicitly modeled and updated using field observations. This Bayesian updating procedure is illustrated through a case study of drilled shafts.