Parametric deep learning model for predicting bearing capacity of strip foundation via neural operator

Abstract Strip foundations, as a widely applied form of shallow foundation, involve foundation displacements and soil deformations under loading, which are critical issues in geotechnical engineering. Traditional limit analysis methods can only provide solutions for ultimate bearing capacity, while numerical methods require remeshing and remodeling for different scenarios. To address these challenges, this study proposes a deep learning approach based on the DeepONet neural operator for rapid and accurate predictions of load–displacement curves and vertical displacement fields of strip foundations under various conditions. A dataset with randomly distributed parameters was generated using finite element method, with the training set employed to train the neural network. Validation on the test set shows that the proposed method not only accurately predicts ultimate bearing capacity but also captures the nonlinear characteristics of high-dimensional data. As an offline model alternative to finite element methods, the proposed approach holds promise for efficient and real-time prediction of the mechanical behavior of shallow foundations under loading.