Integrating deep neural network with elastoplastic analysis: a hybrid approach for slope stability analysis

Abstract

Conventional data-driven methods for slope stability analysis often exhibit an over-reliance on data while neglecting underlying physical principles. To address this limitation, this study proposes a physics-informed neural network (PINN) framework that integrates a neural network surrogate with the elastoplastic deformation mechanism of soil slopes. The governing equations incorporating the elastoplastic constitutive model based on the Mohr–Coulomb yield criterion, along with the boundary conditions, are embedded into the training framework of the PINN. The PINN functions as a surrogate model that requires no pre-constructed training dataset and automatically satisfies both the governing equations and the boundary conditions. Consequently, the developed PINN can directly predict the displacement field of a slope and automatically derive the associated stress–strain fields that comply with the deformation mechanism. These outputs are coupled with the multi-initial point sequential quadratic programming (MSQP) algorithm and the slip surface stress analysis (SSSA) method, enabling the efficient identification of the critical slip surface and the calculation of the corresponding factor of safety (FOS). The proposed method is validated through two illustrative examples. Comparisons of the results with those from commercial software confirm the high accuracy of the proposed method in predicting the stress–strain response and the FOS. This study provides a data-driven and physics-informed paradigm for slope stability analysis grounded in clear physical mechanisms.