## 6 Conclusions  
In this study, two machine learning (ML) classification algorithms; random forest and extremely randomized trees, were employed to characterize and classify ground conditions along the Pahang-Selangor Raw Water Transfer (PSRWT) tunnel alignment in Malaysia based on TBM operating parameters and rock mass data obtained based on JH rock mass  
Figure 11: Confusion matrix of the classification performance of the ERT model. \begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Model** & **Metric** & **Lower (2.5\%)** & **50.0\%** & **Upper (97.5\%)** \\ \hline Random forest & & 0.889 & 0.901 & 0.912 \\ Extremely randomized trees & Kappa & 0.902 & 0.913 & 0.924 \\ \hline Random forest & & 0.935 & 0.942 & 0.949 \\ Extremely randomized trees & Accuracy & 0.944 & 0.950 & 0.956 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: 95% confidence intervals of the overall performance metrics of the ML models. Figure 12: Distribution of kappa and overall accuracy of the ML models obtained from a bootstrap sampling of the test. classification system. The TBM operating parameters included in this approach are rate of penetration, cutterhead torque, cutterhead thrust, cutterhead revolution per minute, hydraulic cylinder stroke speed, boring pressure, and motor amps. Due to imbalance in the rock mass data, an oversampling technique was used to obtain a balanced training dataset for unbiased learning of the machine learning (ML) models. Multi-class classification was done, categorizing the rock mass condition into A, B, CI, and CII classes per the JH system. The JH classification system categorizes rock mass into six classes but the tunnel section from which the dataset was obtained consisted primarily of hard rocks. Consequently, only rock classes consistent with hard rock were encountered and analyzed in this paper. An extension of this study is needed with a dataset that includes all the soft rock mass classes to make the developed models compressive in all ground conditions that the TBM may encounter along the tunnel tract. The main conclusions of this study can be summarized as follows:  
1. The proposed approach was applied to a dataset from the Pahang-Selangor Raw Water tunnel (PSRWT) project in Malaysia. A comparison between the ML model classification results and the measured rock mass classes shows that the proposed approach is effective. The identification and classification accuracies were 95% and 94% for ERT and RF, respectively with kappa values of at least 0.90. 2. A bootstrap comparison of the performance of the two ML models, RF and ERT, indicated no model outperformed the other. Due to the randomized nature of node splitting, ERT is more computationally expensive than RF. Therefore, if the performance of ERT is not significantly better than that of RF, it is recommendable to adopt RF. 3. The most influential TBM operating parameter in classifying the rock mass is the cutterhead RPM followed by cutterhead thrust. The two least influential parameters are stroke speed and rate of penetration. Therefore, TBM thrust and RPM can be adjusted in real-time by determining the rock mass class being excavated using the ML models developed in this paper. 4. From a practical standpoint, the overall results obtained in this study show that the data-oriented approach is a useful tool for on-the-fly rock mass conditions identification, characterization and classification of ground conditions along tunnel alignment. It can be a tool for on-site decision making such as selecting support systems or refining preliminary support systems based on ground condition encountered. 5. Extension of this research should also focus on exploring other ML techniques including deep learning methods as well as developing a framework for operationalizing this approach in TBMs. Conceptualization, S.S.-N., P.E.A.A. and A.G.A.K. ; data curation, S.S.-N. and P.E.A.A. ; formal analysis, A.G.A.K., S.S.-N. and P.E.A.A. ; methodology, S.S.-N., P.E.A.A. and J.W.A.A. ; software, P.E.A.A. and J.W.A.A. ; supervision, H.Z. and Z.Z.