Real-time fusion of multi-source monitoring data with geotechnical numerical model results using data-driven and physics-informed sparse dictionary learning

Development of digital twins is emerging rapidly in geotechnical engineering, and it often requires real-time updating of numerical models (e.g., finite element model) using multiple sources of monitoring data (e.g., settlement and pore water pressure data). Conventional model updating, or calibration, often involves repeated executions of the numerical model, using monitoring data from a specific source or at limited spatial locations only. This leads to a critical research need of real-time model updating and predictions using a numerical model improved continuously by multi-source monitoring data. To address this need, a physics-informed machine learning method called multi-source sparse dictionary learning (MS-SDL) is proposed in this study. Originated from signal decomposition and compression, MS-SDL utilizes results from a suite of numerical models as basis functions, or dictionary atoms, and employs multi-source monitoring data to select a limited number of important atoms for predicting multiple, spatiotemporally varying geotechnical responses. As monitoring data are collected sequentially, no repeated evaluations of computational numerical models are needed, and an automatic and real-time model calibration is achieved for continuously improving model predictions. A real project in Hong Kong is presented to illustrate the proposed approach. Effect of monitoring data from different sources is also investigated.