Contrastive Boundary Learning for Automated Quality Inspection of Shield Tunnel Segment Assembly

Automated quality inspection of shield tunnel segment assembly remains challenging because boundary ambiguity affects joint localization and point-cloud unfolding may weaken native 3D spatial relationships. Accordingly, this paper addresses the research question: How can shield tunnel segment assembly quality be automatically and accurately inspected from 3D point clouds in complex tunnel environments? This paper proposes an approach comprising fine-grained point cloud semantic segmentation without unfolding and a 3D geometric inspection algorithm quantifying intra- and inter-ring dislocations and longitudinal and circumferential joint widths. Wuhan Rail Transit Xingang Line field tests yielded 89.55% mIoU and 95.25% OA for segment-level segmentation, with MAEs of 0.82–1.26 mm and an overall RMSE of 1.08 mm across four indicators. The workflow provides tunnel quality inspectors with a standardized approach to segment assembly assessment, reducing reliance on manual experience. Future research can extend the workflow through cross-project adaptation, efficient computation, and interactive measurement position selection under occlusion.