Prediction of stress-strain behavior of rock materials under biaxial compression using a deep learning approach

Deep learning has significantly advanced in predicting stress-strain curves. However, due to the complex mechanical properties of rock materials, existing deep learning methods have the problem of insufficient accuracy in predicting the stress-strain curves of rock materials. This paper proposes a deep learning method based on a long short-term memory autoencoder (LSTM-AE) for predicting stress-strain curves of rock materials in discrete element numerical simulations. The LSTM-AE approach uses the LSTM network to construct both the encoder and decoder, where the encoder extracts features from the input data and the decoder generates the target sequence for prediction. The mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) of the predicted and true values are used as the evaluation metrics. The proposed LSTM-AE network is compared with the LSTM network, recurrent neural network (RNN), BP neural network (BPNN), and XGBoost model. The results indicate that the accuracy of the proposed LSTM-AE network outperforms LSTM, RNN, BPNN, and XGBoost. Furthermore, the robustness of the LSTM-AE network is confirmed by predicting 10 sets of special samples. However, the scalability of the LSTM-AE network in handling large datasets and its applicability to predicting laboratory datasets need further verification. Nevertheless, this study provides a valuable reference for solving the prediction accuracy of stress-strain curves in rock materials.