## 4 Engineering Application
### Model Performance Analysis
#### 4.3.2 Comparison of Monitoring Parameters  
To compare the impact of different monitoring parameters on the results, we replaced the monitoring parameter in Section 3.2 with cutterhead torque and total thrust as comparative models. Cutterhead torque and total thrust are commonly used detection parameters in the shield construction field. The detection results based on different monitoring parameters are shown in Figure 8, and the anomaly detection performance of each model is shown in Table 4. Firstly, we found that compared to DT_VAE-LSTM (energy), DT_VAE-LSTM (torque) and DT_VAE-LSTM (thrust) are more susceptible to interference, which makes them more prone to false alarms in the models, especially in the case of DT_VAE-LSTM (thrust), which consistently had a precision value below 0.6. Such false alarms can introduce interference to the precise localization of the anomaly range, thereby affecting the credibility of the anomaly detection results. Secondly, DT_VAE-LSTM (thrust) failed to detect abnormal event 1, having a recall value of only 0.53. Thirdly, during the detection process of abnormal event 2, DT_VAE-LSTM (torque) reported the anomaly significantly later than DT_VAE-LSTM (energy). Therefore, DT_VAE-LSTM (thrust) and DT_VAE-LSTM (torque) exhibited a higher rate of missed detections, indicating poorer performance. In conclusion, the \(F1\) score of DT_VAE-LSTM (energy) is higher than the other two comparative models on three abnormal events. Using energy consumption as a monitoring parameter can better reflect the overall status of the shield tunneling process and provide more valuable references for detecting and locating the range of anomalies.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Abnormal Event**} & \multicolumn{2}{c}{**Anomaly**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**\(F1\)**} \\  & **Detection Model** & & & \\ \hline \multirow{4}{*}{Abnormal event 1} & DT\_VAE-LSTM (torque) & 0.87 & **0.98** & 0.92 \\  & DT\_VAE-LSTM (thrust) & 0.64 & 0.53 & 0.58 \\  & DT\_VAE-LSTM (energy) & **1.00** & **0.98** & **0.99** \\ \hline \multirow{4}{*}{Abnormal event 2} & DT\_VAE-LSTM (torque) & 0.83 & 0.46 & 0.60 \\  & DT\_VAE-LSTM (thrust) & 0.62 & **1.00** & 0.77 \\  & DT\_VAE-LSTM (energy) & **0.84** & 0.94 & **0.89** \\ \hline \multirow{4}{*}{Abnormal event 3} & DT\_VAE-LSTM (torque) & 0.43 & 0.68 & 0.52 \\  & DT\_VAE-LSTM (thrust) & 0.44 & **0.87** & 0.58 \\ \cline{1-1}  & DT\_VAE-LSTM (energy) & **0.70** & 0.64 & **0.67** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of anomaly detection models based on different monitoring parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Abnormal Event**} & \multicolumn{2}{c}{**Anomaly**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**\(F1\)**} \\  & **Detection Model** & & & \\ \hline \multirow{4}{*}{Abnormal event 1} & VAE & **1.00** & 0.69 & 0.82 \\  & AE-LSTM & **1.00** & 0.90 & 0.94 \\  & VAE-LSTM & **1.00** & 0.92 & 0.96 \\  & DT\_VAE-LSTM & **1.00** & **0.98** & **0.99** \\ \hline \multirow{4}{*}{Abnormal event 2} & VAE & 0.98 & 0.42 & 0.59 \\  & AE-LSTM & 0.92 & 0.42 & 0.58 \\  & VAE-LSTM & **1.00** & 0.73 & 0.85 \\  & DT\_VAE-LSTM & 0.84 & **0.94** & **0.89** \\ \hline \multirow{4}{*}{Abnormal event 3} & VAE & 0.82 & 0.47 & 0.60 \\  & AE-LSTM & 0.65 & 0.47 & 0.55 \\ \cline{1-1}  & VAE-LSTM & **0.77** & 0.56 & 0.64 \\ \cline{1-1}  & DT\_VAE-LSTM & 0.70 & **0.64** & **0.67** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of different anomaly detection algorithms.