# Automated Tunnel Point Cloud Segmentation and Extraction Method
## 3 Engineering Application
### Engineering Data Processing  
The tunnel arch point cloud is extracted using the arch point cloud extraction method, and the GuSAC algorithm is applied to denoising the point cloud data. After denoising, the tunnel lining and impurity point clouds are effectively distinguished, as shown in Figure 15. In the figure, it can be observed that the denoised tunnel cross-section presents a regular circular shape, with most impurity point clouds removed. The experiment shows that, under the operation based on the segmented centerline, the GuSAC algorithm achieves an average denoising rate of over 95%, with the denoising process being both efficient and reliable. After denoising, the TMatch algorithm is applied to classify the tunnel point clouds into circumferential and inter-ring lining categories. The classification results indicate that the algorithm can accurately identify and segment the tunnel lining point clouds. By extracting the labels, it precisely divides the corresponding lining blocks. In the experiment, for a single ring, with an average of 1,965,348 points cloud data, 1,912,681 points were correctly classified, while only 1796 points remained unclassified on average. The number of misclassified points ranged from 50,000 to 100,000, and the misclassified point clouds only occurred in areas where the crown segment was located at the edge of the tunnel. The overall classification accuracy exceeded 95%. Unclassified point clouds accounted for less than 1% of the total data, having minimal impact on the structural deformation analysis. Figure 14: Raw point cloud data. Additionally, the misclassified point clouds were concentrated in the regions where the tunnel's crown segment was distributed along the pipeline concentration area. In this region, the tunnel was segmented into multiple small modules, resulting in poorer classification performance. However, the algorithm demonstrated high classification accuracy in areas with regular tunnel lining distribution, especially at the crown segment at the top. The TMatch algorithm directly segments point cloud coordinates of individual elements, avoiding the complex processes of image detection and registration. Compared to traditional boundary detection algorithms [27], which first segment images and then align and segment the corresponding point clouds, the TMatch algorithm achieves lower losses and errors while significantly improving the accuracy and efficiency of tunnel point cloud classification. The specific visualization is shown in Figure 16.  
In this study, the accuracy of tunnel lining segmentation results is quantified by calculating the F1 Score and Intersection over Union (IoU). The F1 Score, as the harmonic mean of precision and recall, provides a comprehensive evaluation of segmentation performance. Precision reflects the proportion of correctly predicted tunnel lining points among all predicted lining points, while recall measures the proportion of correctly identified lining points among all actual tunnel lining points. IoU is used to assess the overlap between the predicted region and the ground truth region. These metrics collectively contribute to a comprehensive evaluation of the algorithm's performance in tunnel lining segmentation. However, due to the lack of standardized segmented point cloud data, this study manually created standard data using CloudCompare software based on the results output by the GuSAC algorithm. The segmentation criteria for tunnel lining were primarily  
Figure 16: Visualization of tunnel lining inter-ring and intra-ring segmentation results. Figure 15: Application of engineering data. (**a**) Tunnel structural point cloud. (**b**) Non-tunnel structural point cloud. (**c**) Original tunnel cross-section. (**d**) Cross-section after removal of non-tunnel structural point cloud. based on joint features.