## 4 Validation and Analysis
### Study Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Best Hyper-Parameters**} & \multicolumn{4}{c}{**Test Performance**} & \multirow{2}{*}{**Training**} \\ \cline{3-3} \cline{5-6}  & & **Accuracy** & **Precision** & **Recall** & \(\mathbf{F_{1}}\) & **Time/s** \\ \hline \multirow{4}{*}{KSVM} & Kernel function: Gaussian & & & & & \\  & Kernel scale: 1.05 & & 98.1\% & 98.3\% & 98.5\% & 1316 \\  & Box constraint level: 500 & & 98.1\% & 98.3\% & 98.5\% & 1316 \\  & Penalty factor: 3 & & & & & \\  & Number of splits: 693 & & 95.6\% & 97.1\% & 95.9\% & 96.5\% & 432 \\  & Split criteria: Minimized deviations & & & & & \\  & Number of neighbors: 3 & & & & & \\ KNN & Distance metric: Hamming Distance weight: Inverse & 96.8\% & 99.2\% & 95.8\% & 97.5\% & 866 \\  & Distance Weighting & & & & & \\ NB & Distribution: Kernel & & 72.8\% & 78.3\% & 79.9\% & 79.1\% & 2151 \\  & Kernel type: Gaussian & & & & & \\ CNN & Number of convolutional layer: 6 & 98.9\% & 99.9\% & 98.3\% & 99.1\% & 6500 \\  & Dropout rate: 0.3 & & & & & \\ SAE & Number of stacked autoencoder: 4 & 99.0\% & 99.4\% & 98.9\% & 99.2\% & 5219 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Hyper-parameters and performance results of 6 classification models. cutters needed replacement. As for the KSVM model based on the data of 15 continuous days, the predicted value changed before cutter failure and remained correct when the cutters needed replacement. As for new data after cutter replacement, the half-month model could perceive well the change of cutters' performance. These results show that the proposed KSVM model can generate an average accuracy of 90.0% and an average \(F_{1}\) score of 86.2%, performing well in terms of high accuracy, robustness, and stability. Generally, disc cutter wear processes are entirely different among different tunnel projects. To prove the adaptability of our method, the proposed KSVM model was tested on an earth pressure balance (EPB) shield machine dataset in Guangdong. The geological conditions are different between the Jilin and Guangdong projects [46]. Geologically, the tunnel section in Guangdong is mainly composed of backfill, silty clay, weathered rock, and weathered granite. According to [40], the rock formation of Guangdong tunnel is mainly soft rock, while that of Jilin tunnel is mainly hard rock. The mean value of rock UCS in Guangdong is 32, compared with 163 in Jilin. In addition, the tunneling equipment used in the two projects was quite different. The boring machine in  
Figure 8: ROC curve of classification models on test sets. Figure 9: Comparison of predicted and actual conditions of TBM disc cutters from 6 to 12 June 2016. Guangdong had six more central cutters and its face cutters were aligned rather than misaligned. During tunneling, compared with the Jilin project, the Guangdong boring machine had a greater trusting force, a slower cutterhead rotational speed, and a higher penetration rate. The proposed model can provide 88.8% accuracy and 93.1% \(F_{1}\) score prediction performance, compared with 90.0% accuracy and 86.2% \(F_{1}\) score on the Jilin project. The results show that the proposed model can be deployed in different projects even if the working conditions are highly different.