## 5 Conclusions  
We proposed a novel deep neural network model for rock-mass classification based on depth-sequence MWD data, which can extract information related to the surrounding rock classes in both the forward and reverse directions, thereby improving the accuracy of the surrounding-rock classification prediction. In this model, we selected key parameters through Kendall coefficients and the deep neural network model uses BILSTM as the core, supplemented by two fully connected layers to integrate class-distinct local information, and the 'window selector', 'Voter', and other modules designed in the network to cooperate with one another and improve the generalization ability and prediction performance of the model. In addition, stratified K-fold cross-validation was used for model optimization to avoid overfitting in small datasets. BN was used to solve the ICS problem and improve the model prediction performance and multiple evaluation metrics were used to provide a comprehensive evaluation of the model's performance from various perspectives. After the structural design was completed, we created a dataset based on the advanced drilling data from a highway tunnel in Yunnan Province, China, and the BILSTM-based deep neural network was used to predict the class of the surrounding rock. The model was compared with the MLP and SVM models based on the same dataset, and the performances of the three models were evaluated from different aspects through multiple evaluation \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Macro\_Average\_AUC** & **Micro\_Average\_AUC** & **Difference** \\ \hline BILSTM & 0.9831 & 0.9814 & 0.0017 \\ MLP & 0.9209 & 0.9305 & \(-\)0.0096 \\ SVM & 0.9212 & 0.9176 & 0.0036 \\ \hline \hline \end{tabular} Note: Difference = Macro\_average\_AUC-Micro\_average\_AUC.  
\end{table} 
Table 8: Macro–micro average AUC values. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Training-Set Accuracy** & **Test-Set Accuracy** & **Difference** \\ \hline BILSTM & 0.954 & 0.900 & 0.054 \\ MLP & 0.886 & 0.770 & 0.116 \\ SVM & 0.844 & 0.750 & 0.094 \\ \hline \hline \end{tabular} Note: Difference = Training-set accuracy—Test-set accuracy.  
\end{table} 
Table 9: Predicted results for the training and test sets. metrics. We confirmed that the proposed BILSTM-based deep neural network has the best prediction performance. Specific conclusions are as follows:  
1. Compared with the MLP and SVM models, the BILSTM model has the best prediction ability, with the results of each metric being \(\text{Accuracy}_{\text{10 CV}}=0.896\), \(\text{Accuracy}_{\text{overall}}=0.900\), \(\text{Precision}_{\text{overall}}=0.904\), \(\text{Recall}_{\text{overall}}=0.898\), \(\text{F}_{\text{1-overall}}=0.900\), \(\text{Micro}_{\text{a}verage}_{\text{a}\text{a}\text{c}}=0.9814\), \(\text{Macro}_{\text{a}verage}_{\text{a}\text{c}}=0.9831\), i.e., all significantly higher than those of the MLP and SVM models. 2. The accuracies of the BILSTM model were 0.954 and 0.900 in the training and test sets, respectively, with the difference being 0.054. The difference of the BILSTM model was the smallest compared with those of the MLP and SVM models, meaning that it had the best generalization performance. The other two methods performed well in the training set but poorly in the test set, with poor generalization and overfitting. 3. The average 10-fold CV accuracy of the BILSTM model was slightly lower than that in the test set. This is because the data in the training, validation, and test sets all came from the same tunnel, with the dataset being homogeneous. The results for the validation set were that the average of the 10-fold CV accuracy and the generalization of the BILSTM model were high, while the results for the test set were only single results; it is reasonable that they were slightly higher than the average 10-fold CV accuracy. 4.