Fig 1.
Table 1.
Table 2.
Fig 2.
The stress-strain curves obtained by biaxial compression test.
Fig 3.
The structure of LSTM network [
61].
Fig 4.
AE model architecture.
Fig 5.
LSTM-AE framework.
Fig 6.
Flowchart of LSTM-AE network in stress-strain curve prediction.
Table 3.
Parameters of the LSTM-AE model.
Fig 7.
Processing time series data using the sliding window method, where t represents the label of the time series segment.
Table 4.
The forecasting results in different models.
Fig 8.
Loss on the training and validation sets in the training process.
Fig 9.
Prediction results of different models on specific samples.
(a) LSTM-AE, (b) LSTM, (c) RNN, (d) BPNN, (e) XGBoost.
Fig 10.
Boxplots for the MSE of the different models in the testing set.
Fig 11.
Coefficient of determination of each group of samples for different models on the testing set.
Fig 12.
Coefficients of determination for different models at different friction coefficients.
Fig 13.
Coefficients of determination of different models for special samples.