## 4 Experimental Results and Discussions
### Importance of Geometric Features  
To validate the significance of geometric features, ablation studies were conducted. As presented in Table 9, the inclusion of geometric features yielded significant improvements for Dataset 1, with precision, recall, and F\({}_{\text{1-score}}\) increasing by 12.56%, 14.28%, and 13.36%, respectively. In contrast, their impact on Dataset 2 was minimal, where recall and F\({}_{\text{1-score}}\) rose by only 0.95% and 0.46%, respectively. The observed performance discrepancy can be primarily attributed to variations in tunnel structural conditions and the resulting impacts of preprocessing. In practical engineering scenarios, leakages frequently occur at structural discontinuities, such as ring seams and bolt holes, which present distinct geometric characteristics compared to the relatively uniform tunnel lining. As illustrated by Table 2 and the \"Distance to ellipse\" feature in Figure 10, Dataset 2 exhibited more significant cross-sectional deformation than Dataset 1. This condition necessitated a more aggressive denoising procedure, which inevitably removed numerous points corresponding to ring seams and bolt holes. The loss of critical geometric information hindered the effective identification of leakage features.  
The feature importance analysis, illustrated in Figure 11, further corroborates this explanation. Geometric features demonstrated greater importance in Dataset 1, which, as shown in Figure 10, retained more points associated with seams and bolt holes. Additionally, intensity features were overwhelmingly dominant in Dataset 2. This aligns with the observations in Section 4.5, where the intensity values for leakage and non-leakage areas in Dataset 2 show minimal overlap, making intensity a more discriminative feature for leakage detection.  
While the proposed method demonstrates promising accuracy, two primary limitations were identified that also suggest clear directions for future research. First, a significant overlap in intensity distributions can occasionally occur between leakage areas and the tunnel lining, which diminishes the discriminative power of the intensity feature. A potential mitigation strategy involves developing an effective intensity correction technique to reduce this overlap. Second, for tunnels with severe cross-sectional deformation, the necessarily aggressive denoising process can inadvertently remove the subtle geometric features that are critical for detection. Therefore, future work could focus on designing a deformation-aware denoising algorithm. Such a method would be tailored to preserve the geometric integrity of structural features like ring seams, thereby enhancing the robustness of the feature extraction process.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data** & **Feature Sets** & **Precision** & **Recall** & **F\({}_{\text{1-score}}\)** \\ \hline \multirow{2}{*}{Dataset 1} & Intensity feature & 74.61 & 81.28 & 77.82 \\  & Intensity feature+ & 87.17 & 95.56 & 91.18 \\ \hline \multirow{2}{*}{Dataset 2} & Intensity feature & 97.43 & 97.32 & 97.38 \\  & Intensity feature + & 97.41 & 98.27 & 97.84 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The recall, precision, and F\({}_{\text{1-score}}\) using different feature sets (%).