Geomaterials exhibit highly nonlinear plastic deformation and fracture behaviors. Deep learning offers a promising alternative by exploiting data-driven nonlinear mappings to bypass explicit equation construction. However, existing methods have difficulty in handling noisy small-sample geotechnical data and lack systematic integration of physical priors (e.g., energy conservation, yield conditions) to ensure consistency. In addition, most alternative models are limited to material-scale predictions and need to be combined with traditional numerical methods to solve problems related to boundary conditions, which affects efficiency. This study proposes a novel graph neural network (GNN)-based surrogate model for plasticity-fracture modeling, bridging data-driven learning and physical principles . The framework encodes state information (nodes) and interactions (edges) via a graph structure, enabling efficient evolution prediction of physical fields while embedding interpretable mechanical components. Three numerical examples validate the accuracy and computational efficacy of the proposed model. 1 | Introduction The research objects of geomaterials (such as rocks, soils, etc.) usually exhibit highly nonlinear plastic deformation and fracture behaviors, and their evolution process is jointly controlled by multiscale structural characteristics (such as pores, cracks, joints) and multiphysical field coupling effects (seepage, temperature, chemical corrosion). In the traditio
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