Physics-Informed Neural Network Surrogate Modeling of Pressurized Cavity In Homogeneous and Bilayered Media

The theory of cavity expansion can explain the mechanical response induced by the expansion or contraction of cylindrical cavities that underpin a wide range of geotechnical applications. While analytical methods often impose restrictive assumptions of homogeneity and isotropy, numerical approaches like the Finite Element Method (FEM) offer greater flexibility but can be computationally prohibitive. To address these limitations, we propose a novel Physics-Informed Neural Network (PINN) framework as a surrogate model for pressurized cavity expansion problems in homogeneous and bilayered isotropic and linear-elastic host media, with arbitrary boundary conditions and constitutive parameters. The proposed model combines known governing equations with observational data, which ensures physical consistency while providing mesh-free, high-fidelity solutions for both homogeneous and bi-layered media. FEM simulations were taken as ground truth and prediction results demonstrate robust predictive accuracy across heterogeneous domains. The proposed PINN significantly accelerates the prediction process, achieving up to 160 times faster computation compared to FEM while maintaining competitive accuracy, though discrepancies in shear stress and boundary errors persist due to the non-convexity of the loss function. These challenges can be mitigated by refining the weights of the loss function terms, improving the network architecture, and using advanced optimization techniques.