Stability Prediction of Soil Slopes Based on Digital Twinning and Deep Learning

Abstract

This paper proposes a slope stability prediction model based on deep learning and digital twinning methods. To establish a reliable slope database, 30 actual slopes were collected, and 100 digital twin (DT) models were generated for each actual slope by fine-tuning the slope profiles. The safety factors of all slope samples were calculated using the Limit Equilibrium Methods (LEMs). A convolutional neural network (CNN) regression model was established, and the root mean square error (RMSE) was used as the evaluation indicator. In order to find an excellent CNN model, the K-fold (K = 10) cross-validation was used to compare the predictive effect of 1D CNN and 2D CNN on the slope safety factor. On this basis, CNN models with different network depths were compared. The results showed that the 2D CNN model with six convolutional layers had the best network prediction effect for the slope dataset. To validate the generalization ability of the model, an actual slope was input into the CNN model; its prediction result was 1.0229, and the absolute error with its real safety factor (1.0197) was 0.0032. With the slope stability prediction model proposed in this paper, the safety factor of slopes can be obtained from their geological and physical data, which greatly simplifies the calculation of the safety factor and has great engineering significance.

Keywords

• slope stability
• digital twins
• convolutional neural network
• safety factor
• non-circular slip surface