## 5 A Prediction Model for Rock Mass Classification Ahead of the Driving Face
### Model Parameter Determination  
Model parameters were determined based on the tabulated methodology summarized by [PERSON] et al. (2018) and the Appendix of the Chinese National Standard GB/T 50218-2014 \"Standard for Engineering Rock Mass Classification\" ([PERSON] et al., 2018). The Basic Quality Index (BQ) of the rock mass was calculated using the quantitative parameters \(R_{\rm c}\) (saturated uniaxial compressive strength in MPa) and \(K_{\rm v}\) (rock mass integrity index), following the formula below:  
\[\text{BQ}=100+3R_{\rm c}+250K_{\rm v} \tag{14}\] \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Module Name**} & **Module** & \multirow{2}{*}{**Component Name**} & **Input** & **Output** & **Function Description** \\  & **Level** & & **Dimension** & **Dimension** & **Function Description** \\ \hline Input & 1 & Raw Input & 32 & (1, 32) & Input of original features \\  & 2 & ConvId & (1, 32) & (64, 32) & Extract local features through convolution \\  & 3 & BatchNorm1d & (64, 32) & (64, 32) & Standardize channel features to accelerate training \\  & 4 & ReLU & (64, 32) & (64, 32) & Nonlinear activation \\  & 5 & SEBlock & (64, 32) & (64, 32) & Channel attention \\  & 6 & SpatialAttention1D & (64, 32) & (64, 32) & Spatial attention \\  & 7 & MaxPool1d (2) & (64, 32) & (64, 16) & Down-sampling to reduce the computational load \\ Convolution & 8 & Conv1d & (64, 16) & (128, 16) & Further extract features through convolution \\ module & 9 & ReLU & (128, 16) & (128, 16) & Nonlinear activation \\  & 10 & SEBlock & (128, 16) & (128, 16) & Secondary fine-tuning of channel attention \\  & 11 & SpatialAttention1D & (128, 16) & (128, 16) & Secondary fine-tuning of spatial attention \\  & 12 & Conv1d & (128, 16) & (256, 16) & Increase in the number of convolution channels \\  & 13 & ReLU & (128, 16) & (128, 16) & Nonlinear activation \\  & 14 & SEBlock & (256, 16) & (256, 16) & Final fine-tuning of channel attention \\  & 15 & SpatialAttention1D & (256, 16) & (256, 16) & Final fine-tuning of spatial attention \\  & 16 & AdaptiveMaxPool1d & (256, 16) & (256, 16) & Adaptively fix the output length \\ LSTM module & 17 & LSTM & (16, 256) & (16, 256) & Treat positions as time steps to capture sequence dependencies \\ Multi-head attention module & 18 & MultiheadAttention & (16, 256) & (16, 256) & Focus on different features to enhance the extraction \\  & 19 & Global Average & (16, 256) & 256 & Perform dimensional Average Pooling to generate a global feature vector \\ Classification & 20 & Linear & 256 & 128 & Reduce the dimension through a fully connected layer \\ module & 21 & Dropout & 128 & 128 & Randomly mask 50% of neurons to prevent overfitting \\  & 22 & Linear & 128 & 9 & Output classification results \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Specific structure table of the SE-CNN-LSTM-Attention hybrid model. The saturated uniaxial compressive strength (\(R_{\rm c}\)) is typically lower than the natural-state ultimate strength. As groundwater effects were excluded, the intact rock uniaxial compressive strength (\(\sigma_{\rm cl}\)) was conservatively equated to \(R_{\rm c}\). Parameter values (Table 7) were assigned via simple random sampling. Through basic augmentation, the amount of data becomes four times that of the original data. Then, the original data undergoes 20 rounds of composite augmentation. Eventually, the amount of data obtained is 24 times that of the original data. The specific data augmentation process is shown in Figure 7. This expanded the dataset to 2160 samples (240 per category), divided into training and test sets at a 7:3 ratio. The data distribution is visualized in the scatter plot in Figure 8.