3D Laser Point Cloud-Based Identification of Lining Defects in Symmetric Tunnel Structures

Tunnels, as symmetric structures, are critical components of transportation infrastructure, particularly in mountainous regions. However, tunnel linings are prone to spalling after long-term service, posing significant safety risks. Although 3D laser scanning enables remote measurement of tunnel linings, existing surface fitting methods face challenges such as insufficient accuracy and high computational cost in quantifying spalling parameters. To address these issues, this study leverages the symmetrical geometry of tunnels to propose a curvature variance-based threshold segmentation method using limited point cloud data. First, the tunnel center axis is accurately determined via Sequential Quadratic Programming and the Quasi-Newton method. Noise and outliers are then removed based on geometric properties. Triangular meshes are constructed, and curvature variance is used as a threshold to extract spalling regions. Finally, surface reconstruction is applied to quantify spalling extent. Experiments in both laboratory and fire-damaged tunnel environments demonstrate that the method accurately extracts and quantifies lining spalling, with an average error of approximately 9.70%. This study underscores the potential of the proposed approach for broad application in tunnel inspection, as it will provide a basis for assessing the structural safety of tunnel linings.