Tunnel Deformation Inspection via Global Spatial Axis Extraction from 3D Raw Point Cloud

Abstract

Global inspection of large-scale tunnels is a fundamental yet challenging task to ensure the structural stability of tunnels and driving safety. Advanced LiDAR scanners, which sample tunnels into 3D point clouds, are making their debut in the Tunnel Deformation Inspection (TDI). However, the acquired raw point clouds inevitably possess noticeable occlusions, missing areas, and noise/outliers. Considering the tunnel as a geometrical sweeping feature, we propose an effective tunnel deformation inspection algorithm by extracting the global spatial axis from the poor-quality raw point cloud. Essentially, we convert tunnel axis extraction into an iterative fitting optimization problem. Specifically, given the scanned raw point cloud of a tunnel, the initial design axis is sampled to generate a series of normal planes within the corresponding Frenet frame, followed by intersecting those planes with the tunnel point cloud to yield a sequence of cross sections. By fitting cross sections with circles, the fitted circle centers are approximated with a B-Spline curve, which is considered as an updated axis. The procedure of “circle fitting and B-SPline approximation” repeats iteratively until convergency, that is, the distance of each fitted circle center to the current axis is smaller than a given threshold. By this means, the spatial axis of the tunnel can be accurately obtained. Subsequently, according to the practical mechanism of tunnel deformation, we design a segmentation approach to partition cross sections into meaningful pieces, based on which various inspection parameters can be automatically computed regarding to tunnel deformation. A variety of practical experiments have demonstrated the feasibility and effectiveness of our inspection method.

Denoising algorithm for inclined tunnel point cloud data based on irregular contour features

“…Therefore, an effective denoising method is needed to remove objects other than the tunnel wall. Currently, researching in this area mostly focuses on shield tunnels, which are approximately standard circles, and commonly using ellipsoid fitting methods for noise removal (Xu et al 2018, Cui et al 2019, Yi et al 2020, Zhang et al 2024. Researching on denoising in mine tunnel mostly involves the removal of drifting points, isolated points, and mixed points (Jing et al 2018.…”

Automatic Detection and Association Analysis of Multiple Surface Defects for Shield Subway Tunnel

“…It can assess the condition of the tunnel lining structure through the three-dimensional modeling of the tunnel. It is widely used in the detection of various surface defects, such as spalling defect [17], water leakage defect [18] and deformation [19]. The principle of the thermal imaging-based method is to convert the temperature distribution of the tunnel lining surface into a visual image through an infrared thermal imager, which can reflect some of the leakage and cavity areas on the shield tunnel.…”

Feasibility of Accurate Point Cloud Model Reconstruction for Earthquake-Damaged Structures Using UAV-Based Photogrammetry

“…Te health monitoring of the structural movement can be realized by comparing the data collected by the UAV over diferent periods [38]. Te semantic segmentation of important structural components in the point cloud model can be accomplished for diferent types of structures, such as bridges [39], tunnels [40], buildings [41], and towers [42]. Structural damage inspection of structural components, such as beams, columns, and walls, is performed easily after structural semantic segmentation in the point cloud model [43].…”