A surrogate model based on deep convolutional neural networks for solving deformation caused by moisture diffusion

Abstract

Moisture diffusion is a common phenomenon in geotechnical engineering, and its induced deformation seriously affects the stability of the engineering structure, such as embankment slope instability and tunnel surrounding rock deformation. Numerical simulation is an effective method for moisture diffusion-deformation coupling computation, but it has a large computational cost and a high learning threshold for ordinary engineers. In this paper, a surrogate model based on deep convolutional neural networks is presented for moisture diffusion-deformation coupling computation. First, designing the neural network structure includes the dense blocks and transition layers, and hyperparameters. Then, the moisture diffusion-deformation coupling model in the finite discrete element method (FDEM) software package MultiFracS is used to obtain the high-fidelity simulation data for the moisture diffusion-deformation examples. The simulation data and the key parameter (elastic modulus and Poisson's ratio) are processed into the image data structure (matrix) for training the surrogate model. Finally, the root means square error and the correlation coefficient are used to evaluate the effectiveness of the surrogate model. The results reveal that, rather than taking several hours to run a numerical model, the surrogate model only takes a few seconds to obtain the deformation and stress under a given moisture field and material parameters, which significantly improves prediction efficiency. Using this surrogate model, the engineers can obtain the deformation law just only modifying key parameters. Moreover, the surrogate model can be packaged into a mobile app to provide support for rapid decision-making on the project site.