Data-driven and physics-informed Bayesian learning of spatiotemporally varying consolidation settlement from sparse site investigation and settlement monitoring data

A digital twin of a geotechnical project (e.g., a reclamation or ground improvement project) is a virtual model that aims to continuously learn from actual observations (e.g., site investigation and monitoring data) and improve model prediction (e.g., spatiotemporally varying consolidation settlement). However, real geotechnical observation data obtained from a site are often spatially sparse (e.g., site investigation data) and spatiotemporally varying (e.g., settlement monitoring data). The sparse and spatiotemporally varying data pose great challenges for continuous learning of data and improvement in model prediction. To address these challenges, this study proposes a novel data-driven and physics-informed Bayesian learning framework that automatically develops ground models from spatially sparse site investigation data, performs geotechnical analysis, and integrates geotechnical analysis results with limited, but spatiotemporally varying, settlement monitoring data to improve model prediction in a systematic and quantitative manner. The proposed method contains three key components, (1) data-driven ground modeling by Bayesian compressive sampling (BCS) using sparse site investigation data as input, (2) finite element modeling (FEM) of consolidation settlement that incorporates domain knowledge, and (3) Bayesian sparse dictionary learning of settlement monitoring data together with FEM results. The proposed method is illustrated using a real ground improvement project, and the results show that the proposed approach performs well. © 2023 Elsevier Ltd.