Development of a Long-term Ground Settlement Prediction Model Using Physics-Informed Neural Networks
This study proposes a physics-informed neural network framework considering creep settlement (Creep-PINN) to accurately predict the long-term settlement behavior of soft clay deposits using only limited field measurements acquired during the early stage of embankment construction, thereby enabling efficient construction process management. Conventional empirical approaches and RNN-based time-series deep learning models generally require long-term field measurements obtained near the completion of consolidation to yield reliable predictions, while existing PINN-based settlement models remain limited in directly utilizing soil property information and in representing creep settlement associated with secondary consolidation. In contrast, the proposed model enforces the consolidation governing equation and staged embankment loading history as physics-based constraints during training and numerically incorporates the secondary compression (creep) mechanism to ensure physical consistency. In particular, an axisymmetric unit-cell u-PINN is employed to approximate the excess pore-water pressure field, and a CoeffNet is coupled to estimate consolidation parameters from site-specific soil properties, thereby accounting for spatial heterogeneity. Scenario-based validation using field monitoring data from OO Port demonstrates that, even within early prediction windows corresponding to initial degrees of consolidation of 10% and 30%, the proposed Creep-PINN achieves lower prediction errors ( = 10%, MAPE = 5.44%) than the Asaoka and hyperbolic methods. These results demonstrate that the proposed framework enables accurate prediction of long-term settlement immediately after embankment placement, supporting more informed decision-making regarding the timing of subsequent construction activities and reducing construction-related risks.