Methodology for Extraction of Tunnel Cross-Sections Using Dense Point Cloud Data

Abstract

Tunnel deformation monitoring is a crucial task to evaluate tunnel stability during the metro operation period. Terrestrial Laser Scanning (TLS) can collect high density and high accuracy point cloud data in a few minutes as an innovation technique, which provides promising applications in tunnel deformation monitoring. Here, an efficient method for extracting tunnel cross-sections and convergence analysis using dense TLS point cloud data is proposed. First, the tunnel orientation is determined using principal component analysis (PCA) in the Euclidean plane. Two control points are introduced to detect and remove the unsuitable points by using point cloud division and then the ground points are removed by defining an elevation value width of 0.5 m. Next, a z-score method is introduced to detect and remove the outlies. Because the tunnel cross-section’s standard shape is round, the circle fitting is implemented using the least-squares method. Afterward, the convergence analysis is made at the angles of 0°, 30° and 150°. The proposed approach’s feasibility is tested on a TLS point cloud of a Nanjing subway tunnel acquired using a FARO X330 laser scanner. The results indicate that the proposed methodology achieves an overall accuracy of 1.34mm, which is also in agreement with the measurements acquired by a total station instrument. The proposed methodology provides new insights and references for the applications of TLS in tunnel deformation monitoring, which can also be extended to other engineering applications.

Keywords

• |cross-section|control point|convergence analysis|z-score method|terrestrial laser scanning|dense point cloud data