Damage Extraction of Metro Tunnel Surface from Roughness Map Generated by Point Cloud

Abstract

Structural damages on the surface of metro tunnel will affect service time and traffic safety seriously. Therefore, it is of great significance to study the method of damage detection. Based on roughness map, a new method for tunnel damage detection is proposed, which is mainly to determine the location of disease. Firstly, this paper uses central axis denoising algorithm to eliminate ancillary facilities. Then, based on the theory of Poisson reconstruction, an irregular triangulated grid of point cloud is constructed in order to calculate the area of polygons of first-order neighborhood for each point as surface area. Next, given that the standard cylinder of tunnel design shape, point cloud, which is projected onto the fitted cylinder, will be constructed grid to calculate projection area around each point. Finally, with the definition of the ratio between the surface area and the projected area, tunnel surface roughness can be extracted under the set threshold. Through experimental analysis for actual tunnel data, the feasibility and accuracy of the method is confirmed.