Efficient Updating of Consolidation-Induced Responses by Auxiliary Bayesian Approach

Predicting the consolidation-induced responses (e.g., settlement and excess pore pressures) is a challenging task due to the existence of soft soils and various geotechnical-related uncertainties. To reduce these uncertainties, observational data obtained at different monitoring moments can be used to update these responses, for example, through Bayesian methods. Nevertheless, Bayesian updating of consolidation behaviors of soft soils can be computationally demanding when sophisticated computational models are involved, and a great number of model evaluations are required for the updating given a monitoring dataset. This becomes more challenging if multiple different datasets (e.g., those sequentially obtained at different monitoring moments) are concerned, for each of which a Bayesian updating run is needed. This paper develops a novel simulation-based Bayesian framework that allows efficient updating of soft soil behaviors based on different datasets. It consists of two major components: (1) driving Bayesian analysis to generate problem-specific information on the response evaluations based on the monitoring dataset obtained at early monitoring moments; and (2) target Bayesian analysis to update the soft soil behaviors given new datasets obtained at latter monitoring moments by making use of the information generated in the first step, which requires negligible computational efforts. A consolidation example of clay is adopted to demonstrate the efficiency and rationality of the proposed approach. Effects of monitoring datasets with different types and locations on the updated response are also investigated.

Keywords: Bayesian updating, BUS, consolidation response, monitoring dataset.