## 5 A Prediction Model for Rock Mass Classification Ahead of the Driving Face
### Intelligent Prediction Fundamentals for Rock Mass Classification  
#### 5.1.1 SE-CNN Architecture  
The Squeeze-and-Excitation Network (SENet), proposed by [PERSON] et al. [35], introduces a channel attention mechanism to explicitly model interdependencies between feature channels in convolutional neural networks (CNNs). By embedding SENet modules after CNN convolutional layers, the resulting SE-CNN architecture enhances feature representation by adaptively recalibrating channel-wise feature responses, thereby amplifying critical features while suppressing less relevant ones. This mechanism is particularly effective for extracting instability-related patterns from simulation data in rock mass classification tasks. The SENet module comprises four key components, as shown in Figure 6a.  
Figure 5: The flowchart of the program for predicting the surrounding rock in front of the tunnel face using LDP curves.  
(1) Transformation  
A standard convolutional operation generates uncalibrated feature maps, \(\mathbf{U}\in\ \mathbf{R}^{H\times W\times C}\), where \(H\), \(W\), and \(C\) denote the height, width, and channel dimensions, respectively.  
(2) Squeeze Operation  
Global Average Pooling (GAP) aggregates spatial information within each channel to produce channel-wise statistics \(\mathbf{z}\in\mathbf{R}^{C}\). For the \(c\)-th channel, the following is calculated:  
\[z_{c}=\mathbf{F}_{\text{squeeze}}(\mathbf{u}_{c})=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j =1}^{W}u_{c}(i,j) \tag{9}\]  
This equation eliminates spatial interference while preserving channel-wise relationships, forming a compressed descriptor that captures global contextual information for tunnel face surrounding rock prediction.  
Figure 6: Schematic diagram of the complete structure of the hybrid architecture neural network.  
(3) Excitation Operation  
A gating mechanism with two fully connected (FC) layers learns nonlinear channel dependencies as follows:  
\[\mathbf{s}=\mathbf{F}_{\text{excitation}}(\mathbf{z},\mathbf{W})=\sigma[g(\mathbf{z},\mathbf{W})]=\sigma[ \mathbf{W}_{2}\delta(\mathbf{W}_{1}\mathbf{z})] \tag{10}\]  
where \(\mathbf{W}_{1}\in\mathbb{R}^{(C/r)\times C}\) and \(\mathbf{W}_{2}\in\mathbb{R}^{C\times(C/r)}\) are learnable weights; \(\mathbf{\delta(\cdot)}\) denotes ReLU activation; \(\sigma(\cdot)\) is the sigmoid function; and \(r\) is a reduction ratio controlling model complexity.  
(4) Scale Operation  
The learned channel-wise weights \(s\) were applied to the original feature map \(U\) via element-wise multiplication across channels, generating the recalibrated output \(\mathbf{\widetilde{X}}\):  
\[\mathbf{\widetilde{x}}_{c}=\mathbf{F}_{\text{scale}}(\mathbf{u}_{c},s_{c})=s_{c}\mathbf{u}_{c} \tag{11}\]  
\[\mathbf{\widetilde{X}}=[\mathbf{\widetilde{x}}_{1},\cdots,\mathbf{\widetilde{x}}_{c}, \cdots,\mathbf{\widetilde{x}}_{C}] \tag{12}\]  
The SENet module was designed to explicitly model inter-channel relationships within neural networks. Through Global Average Pooling (GAP) and fully connected layers, SENet adaptively learns channel-wise weights to amplify informative feature channels critical to target tasks while suppressing less contributive ones, thereby enhancing model performance [35].