Prediction of Shear Strength of Silty Clay in Seasonally Frozen Regions Based on SSC-PINN
Abstract
The prediction of shear strength in seasonally frozen silty clay is restricted by complex physical mechanisms and sparse experimental data. A self-supervised contrastive physics-informed neural network is proposed to overcome these limitations. Robust latent features are extracted from limited datasets via contrastive pretraining. Time-dependent constitutive equations and physical boundary conditions are simultaneously embedded into the loss function. This mathematical constraint ensures strict physical consistency during the modeling process. The proposed framework was validated using 100 independent laboratory samples prepared under controlled moisture content, freezing temperature, and thawing duration. The experimental results demonstrate the superior predictive accuracy of the proposed model. A coefficient of determination (R2) of 0.988 was achieved on the test set, accompanied by minimized error metrics compared to conventional data-driven approaches. Consequently, a highly accurate and reliable methodology is established by this architecture for evaluating soil stability and supporting infrastructure design in cold regions.