A High-Precision Method for Extracting Lateral Deformation in Operational Shield Tunnels Based on LiDAR Point Cloud Analysis

Deformation monitoring is critical for structural health assessment of operational shield tunnels in urban rail transit. LiDAR point clouds in operating tunnels usually contain auxiliary facilities, occlusions, noise, and uneven point density. Conventional section-by-section ellipse fitting often leads to unstable parameter jumps between adjacent sections. This paper presents a high-precision method to extract lateral deformation from tunnel LiDAR point clouds. First, a point-wise attention Transformer network (PWAT) is proposed based on PointNet++ for lining segmentation, using k-NN adaptive sampling, geometric position encoding, and geometry-constrained multi-head self-attention. Second, a continuity-constrained RANSAC (CC-RANSAC) algorithm is developed to improve ellipse parameter stability by adding continuity penalties between neighboring sections. Experiments were carried out on a Shanghai metro shield tunnel. Results show that PWAT achieves 99.53% overall accuracy and 99.06% mIoU in six-class segmentation. CC-RANSAC reduces the mean residual to 2.0 mm and the center jump rate to 4.2%. Compared with total station data, the mean absolute error and root mean square error are 1.35 mm and 1.68 mm. The proposed method can automatically and accurately extract lateral deformation for operational shield tunnels.