# TBM Enclosure Rock Grade Prediction Method Based on Multi-Source Feature Fusion
## 4 Results and Discussion
### Discussion
It significantly reduces the critical misclassification of Class IV rocks as Class III--minimising safety risks from insufficient support--while also exhibiting fewer instances of mislabelling Class III rocks as Class IV, thereby avoiding unnecessary construction costs. These findings underscore TRNet's effectiveness in handling asymmetric error costs, making it the most reliable choice for real-world applications where misclassification \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Accuracy** & **AUC** & **Kappa** & **Epoch** & **Train Duration/s** \\ \hline TRNet & 0.92144275 & 0.95899427 & 0.7455987 & 12 & 6.0561 \\ CNN & 0.91159420 & 0.94224364 & 0.70426115 & 28 & 5.3334 \\ LSTM & 0.92113913 & 0.88156809 & 0.74363058 & 16 & 5.3498 \\ KAN & 0.90304347 & 0.92731085 & 0.72062828 & 12 & 5.8756 \\ KNN & 0.8636 & 0.7298 & 0.5424 & N/A & 0.01 \\ XGBoost & 0.8563 & 0.8823 & 0.6518 & N/A & 0.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of prediction performance of different algorithms on the test set. consequences are substantial. Its lowest total misclassifications further demonstrate its superiority in practical engineering scenarios. To further demonstrate the superiority of the fusion strategy combining various network modules, we conducted ablation experiments on TRNet. The prediction performance of the model was compared after each network module was removed in a gradual manner, with TRNet serving as the benchmark. The findings, presented in Table 9, demonstrate that TRNet attains the optimal prediction performance, while the accuracy of the model experiences a decline following the removal of each network module. The results demonstrate the relative importance of various network modules to the prediction ability of TRNet as follows: Direct-LiNGAM > CNN > KAN. The findings demonstrate that each network module incorporated into TRNet contributes positively to its prediction ability, thereby validating the efficacy of integrating multi-source feature information and illustrating the consideration of the intrinsic connection between data importance. Furthermore, the incorporation of Direct-LiNGAM in constructing the local causality matrix has been identified as the most significant contributor to the model's prediction capability. This outcome signifies that the consideration of causality between multi-source data can enhance not only the perception of the intrinsic pattern within the data but also the capacity to provide a comprehensive explanation. Figure 14: Evaluation scores and ROC curves. (**a**) Evaluation scores for various classifiers; (**b**) ROC curves for various classifiers. Figure 15: Confusion matrix of six algorithmic classifiers on the test set. (**a**) TRNet; (**b**) CNN; (**c**) LSTM; (**d**) KAN; (**e**) KNN; (**f**) XGBoost. respectively, indicating that they capture most of the data variability. The top-right curve demonstrates that the cumulative explained variance rapidly approaches 1, suggesting that only a few principal components are sufficient to retain the major information. The bottom-left bar chart compares the performance of the original feature model and the PCA-based model in terms of accuracy, AUC, and Kappa. The bottom-right bar chart visually shows the reduction in feature dimensions from 12 to 7. The findings indicate that the performance of the model following PCA dimension reduction is analogous to that of the baseline model with respect to accuracy and AUC, though Kappa does exhibit a slight decrease, which may be attributable to the loss of classification-related information during the dimension reduction process. Considering the current multi-source feature set, dimensionality reduction is not necessary, which further confirms the validity and effectiveness of the selected features in this study. In order to further enhance the interpretability of the TRNet model, the SHAP method is employed to investigate the influence of selected features on the prediction results and to quantify the degree of influence.