Mastering the mechanical properties of frozen soil under complex stress states in cold regions and establishing accurate constitutive models to predict the nonlinear stress-strain relationship of the soil under multi-factor coupling are key to ensuring the stability and safety of engineering projects. In this study, true triaxial tests were conducted on roadbed peat soil in seasonally frozen regions under different temperatures, confining pressures, and b-values. Based on analysis of the deviatoric stress-major principal strain curve, the variation patterns of the intermediate principal stress, volumetric strain and minor principal strain deformation characteristics, and anisotropy of deformation, as well as verification of the failure point strength criterion, an intelligent constitutive model that describes the soil's stress-strain behavior was established using the Transformer network, integrated with prior information, and the robustness and generalization ability of the model were evaluated. The results indicate that the deviatoric stress is positively correlated with the confining pressure and the b-value, and it is negatively correlated with the freezing temperature. The variation in the intermediate principal stress exhibits a significant nonlinear growth characteristic. The soil exhibits expansion deformation in the direction of the minor principal stress, and the volumetric strain exhibits shear shrinkage. The anisotropy of the specimen induced by stress is negatively correlated with temperature and positively correlated with the bvalue. Three strength criteria were used to validate the failure point of the sample, and it was found that the spatially mobilized plane strength criterion is the most suitable for describing the failure behavior of frozen peat soil. A path-dependent physics-informed Transformer model that considers the physical constraints and stress paths was established. This model can effectively predict the stress-strain characteristics of soil under different working conditions. The prediction correlation of the model under the Markov chain Monte Carlo strategy was used as an evaluation metric for the original model's robustness, and the analysis results demonstrate that the improved model has good robustness. The validation dataset was input to the trained model, and it was found that the model still exhibits a good prediction accuracy, demonstrating its strong generalization ability. The research results provide a deeper understanding of the mechanical properties of frozen peat soil under true triaxial stress states, and the established intelligent constitutive model provides theoretical support for preventing engineering disasters and for early disaster warning.

来源平台：COLD REGIONS SCIENCE AND TECHNOLOGY