## 5 Results and Discussion  
### Classification Performance the ML Models  
The overall performance of the ML models was measured by the accuracy and [PERSON]'s kappa. These were calculated by considering all the correct predictions and all the wrong predictions. The performance of the models in predicting each rock class was measured by the F1-score and balanced accuracy. Since the study involved a multi-class classification, the performance metrics were computed by considering one class, e.g., class A, as positive, while the other three classes, e.g., classes B, CI, and CII, were considered negative. This was done until each rock mass class was considered positive to obtain the metrics presented in Table 6.  
Based on the F1-score and the balanced accuracy, both RF and ERT accurately predicted rock class CII with at least 96% in terms of the F1-score and 99% in terms of the balanced accuracy. The worse model performance was recorded when predicting class A with F1-score of at least 92% and balanced accuracies of 95%. The variation in performance level in predicting the different rock mass classes could be related to the TBM operation and excavation process. Rock mass class A consists of slightly w \begin{table}
\begin{tabular}{l l l l l} \hline \hline  & \multicolumn{2}{c}{**Random Forest**} & \multicolumn{2}{c}{**Extremely Randomized Trees**} \\ \hline
**Rock Class** & **F1 Score** & **Balanced Accuracy** & **F1 Score** & **Balanced Accuracy** \\ \hline Class: A & 0.92 & 0.95 & 0.93 & 0.95 \\ Class: B & 0.95 & 0.94 & 0.96 & 0.95 \\ Class: CI & 0.95 & 0.97 & 0.96 & 0.97 \\ Class: CII & 0.96 & 0.99 & 0.97 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance metrics of the ML models in predicting each rock class. fractures, to fresh massive granites, which causes excessive cutter wear resulting in frequent replacement of consumable components, e.g., cutters. This wear and tear, and subsequent replacement of cutters can cause fluctuations in the TBM operating parameters and could have resulted in the low prediction performance of class A as can be seen Table 6. As the rock mass gets highly weathered and intensively fractured, like rock mass classified as CII, less cutter wear will be observed, resulting in a fairly consistent set of operating parameters, all other factors held constant. This can also be seen in Figure 6, where the boring pressure showed less variability in class CII as compared to the rest of the rock mass categories. In general, more consistent set of operating parameters should lead to models with high prediction performance. In terms of classifying the overall rock mass in the various rock mass classes, both models performed very well with the overall accuracy greater than 0.94 and [PERSON]'s kappa greater than 0.90, as shown in Table 7.  
Visual presentation of the classification by the two models in the form of confusion matrix heatmaps are shown in Figures 10 and 11. The counter-diagonal boxes (top right to the bottom left corner) represent correct prediction of the rock mass class while the rest of the boxes represent misclassification. The intensity of the fill color of the boxes represents the proportion of the data points that have been categorized into that class by the model (misclassification and correct classification). It is interesting to note that in both models, class A was only misclassified as class B but was not CI or CII (Figures 10 and 11). Class B was misclassified as A and CI on a few occasions and only misclassified as CII once. The misclassifications of CI were mostly as B with only one being labeled as class A by RF and three labeled as CII. Both models only misclassified CII as CI once. This shows that the models do not predict rock classes that are far off from the actual class, especially in the case of A and CII. This means that on a very worst-case scenario of misclassification, there is still confidence that the prediction is within the immediate neighborhood of the actual rock mass class.