Real-time model updating and prediction of three-dimensional time-varying consolidation settlement using machine learning

The development of digital twins for geotechnical structures necessitates the real-time updates of three-dimensional (3D) virtual models (e.g. numerical finite element method (FEM) model) to accurately predict time-varying geotechnical responses (e.g. consolidation settlement) in a 3D spatial domain. However, traditional 3D numerical model updating approaches are computationally prohibitive and therefore difficult to update the 3D responses in real time. To address these challenges, this study proposes a novel machine learning framework called sparse dictionary learning (T-3D-SDL) for real-time updating of time-varying 3D geotechnical responses. In T-3D-SDL, a concerned dataset (e.g. time-varying 3D settlement) is approximated as a linear superposition of dictionary atoms generated from 3D random FEM analyses. Field monitoring data are then used to identify non-trivial atoms and estimate their weights within a Bayesian framework for model updating and prediction. The proposed approach enables the real-time update of temporally varying settlements with a high 3D spatial resolution and quantified uncertainty as field monitoring data evolve. The proposed approach is illustrated using an embankment construction project. The results show that the proposed approach effectively improves settlement predictions along temporal and 3D spatial dimensions, with minimal latency (e.g. within minutes), as monitoring data appear. In addition, the proposed approach requires only a reasonably small number of 3D FEM model evaluations, avoids the use of widely adopted yet often criticized surrogate models, and effectively addresses the limitations (e.g. computational inefficiency) of existing 3D model updating approaches.