Thermomechanical Field Prediction in Subbase Materials Using Physics-Informed Neural Networks
To address the complexities associated with highly nonlinear phase transitions in subbase materials subjected to unidirectional freezing—where direct observation of internal states is challenging—and the limitations of conventional numerical methods in concurrently reconstructing multiphysical fields from sparse experimental data, this study investigates saturated subbase materials from the Dashixia Project. Laboratory freezing experiments were performed, and a two-dimensional axisymmetric physics-informed neural network (PINN) model integrating thermomechanical behavior, phase transition, and deformation was developed. The model incorporated the heat conduction equation (accounting for latent heat), the freezing fraction relaxation equation, and the effective deformation equation into the loss function, employing a continuous freezing fraction to represent phase-change phenomena. Additionally, constraints on the average displacement of the top surface, enhanced sampling near the freezing front, and a dynamic weighting strategy were introduced to improve model performance. This approach enabled the simultaneous prediction of temperature, freezing fraction, and displacement fields. The model achieved a temperature prediction root mean square error (RMSE) of 4.112 °C with an R2 of 0.600, and a top-surface displacement prediction RMSE of 0.0051 with an R2 of 0.993. Observations indicated that the freezing front predominantly progressed from the top downward, while the displacement field exhibited a partitioned pattern characterized by negative displacement in the lower region and positive displacement in the upper region. The proposed methodology offers a novel framework for multi-field reconstruction and frost heave prediction in subgrade materials under conditions of limited observational data.