Automatic Detection Method for Shield Tunnel Segment Dislocation Based on Facility Point Cloud Removal and Segment...

Mobile laser scanning (MLS) has become an effective technique for deformation monitoring in subway shield tunnels. Among various deformation characteristics, segment dislocation is an important indicator of tunnel structural health because it reflects the relative deformation between adjacent segments and may affect the mechanical behavior and waterproof performance of segmental joints. However, existing MLS-based methods for dislocation detection still suffer from facility interference, inaccurate seam localization, and limited automation in quantitative analysis. To address these challenges, this study proposes an automated method for shield tunnel segment dislocation detection based on MLS point cloud processing. The proposed framework consists of three main steps. First, a point cloud filtering strategy integrating offset features and semantic segmentation is developed to remove facility-related noise while preserving tunnel wall information. Second, a tunnel segment segmentation method combining bolt hole extraction and moving template matching is introduced to achieve accurate localization of both horizontal and longitudinal seams, where bolt holes are identified using normal vector and distance constraints. Finally, automated segment dislocation analysis is performed based on the filtering and segmentation results. Experimental results demonstrate that the proposed filtering method improves accuracy by 8.7% and 5.6% compared with conventional ellipse fitting and cylinder fitting methods, respectively. Using manually interpreted reference values derived from the same MLS dataset as the evaluation reference, the proposed method achieves less than 2 mm deviation in both seam localization and dislocation analysis, demonstrating high consistency with manual interpretation. Compared with existing automatic approaches, the proposed method provides more accurate and reliable automated dislocation analysis, significantly reducing the need for manual inspection. The proposed method enhances the automation, consistency, and reliability of shield tunnel deformation assessment and provides an effective solution for structural health monitoring.