## 4 Engineering Case Application
### Rock-Mass Classification Prediction  
In addition to the BILSTM-based deep neural network constructed in the previous section, we also selected two classic traditional machine learning methods, MLP and \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Key Drilling Parameters** & **Unit** & **Max** & **Min** & **Mean** & **Median** \\ \hline Penetration rate of drilling & m/min & 4.96 & 0.00 & 0.42 & 0.31 \\ Torque & kN-m & 1.87 & 0.02 & 0.68 & 0.65 \\ Thrust & kN & 36.30 & 0.00 & 8.93 & 8.80 \\ Penetration energy & \(\backslash\) & 423.00 & 0.00 & 376.05 & 392.00 \\ EV & \(\backslash\) & 6448.00 & 0.00 & 239.98 & 201.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistics of the key drilling parameters in the training set. Figure 14: Frequency distribution of the training set. Figure 15: Frequency distribution of the test set. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Key Drilling Parameters** & **Unit** & **Max** & **Min** & **Mean** & **Median** \\ \hline Penetration rate of drilling & m/min & 3.69 & 0.00 & 0.47 & 0.32 \\ Torque & kN-m & 1.72 & 0.05 & 0.65 & 0.62 \\ Thrust & kN & 21.00 & 0.00 & 8.79 & 8.60 \\ Penetration energy & \(\backslash\) & 410.00 & 0.00 & 378.83 & 388.00 \\ EV & \(\backslash\) & 6448.00 & 0.00 & 227.87 & 198.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistics of the key drilling parameters in the test set. SVM, as comparative methods to predict the rock mass classification. The BILSTM deep neural network was implemented using the TensorFlow framework [35]. The MLP model, as a comparison model, was implemented using TensorFlow and the SVM model was implemented using Scikit-Learn. To ensure comparability between the different methods, the training, validation, and test sets of the three methods were identical, and the evaluation metrics for the three methods were also identical. Figure 16 shows the workflow of the rock-mass classification prediction for each model. We selected the optimized hyperparameters using the grid-search method [36] to ensure that the resulting hyperparameters are the best possible. Accuracy was used as the evaluation metric to perform hyperparameter tuning based on the stratified 10-fold CV. The tuning results are shown in Table 6, with parameters not marked being the default values. The stratified 10-fold CV accuracies of the three methods are shown in Figure 17a; the mean accuracy is shown in Figure 17b, from which it can be seen that the model accuracies were BILSTM > MLP > SVM, indicating that the BILSTM model had the best ability to identify the surrounding rock classes. The model with the highest accuracy in the 10-fold model was used as the prediction model. Based on the results shown in Figure 17a, the BILSTM, MLP, and SVM selected the fold7, fold9, and fold3 models as rock-class prediction models for the test set. Figures 18 and 19 illustrate the training processes for the prediction models of BILSTM and MLP, respectively. From the figure, it can be seen that the accuracy and loss curves of the BILSTM were stable, indicating that the BILSTM model proposed in this paper was reliable without overfitting; the curve of the MLP was not so stable and the loss curve of the validation set had an upward trend at the end, and there were overfitting phenomena. The actual rock classes for the test set and the prediction results of the three methods are shown in Figure 20; scatter plots are shown in Figure 21.  
Figure 19: Accuracy and Loss curves on training set and validation set of MLP neural network. (**a**) Accuracy curve and (**b**) Loss curve. Figure 17: Cross-validation accuracies. (**a**) Stratified 10-fold CV accuracies and (**b**) Stratified 10-fold CV mean accuracies. Figure 18: Accuracy and Loss curves on training set and validation set of BILSTM-based deep neural network. (**a**) Accuracy curve and (**b**) Loss curve.