Shield Tunnel Dislocation Detection Method Based on Semantic Segmentation and Bolt Hole Positioning of MLS Point Cloud

The dislocation of ring segments in shield tunnels poses adverse effects on tunnel structural stability and waterproofing. Current methods can only detect limited types of dislocation from circular tunnels, posing challenges in meeting practical requirements. This article introduces a dislocation detection method that is applicable to shield tunnels of various shapes, enabling the detection of both inter-ring and intra-ring dislocations. First, a point cloud lossless unfolding method is introduced, allowing for the unfolding of tunnel point clouds of any cross-sectional shape without compromising point cloud features. Subsequently, a subway tunnel point cloud segmentation workflow is proposed, enabling the direct application of image deep learning (DL) networks to tunnel point clouds. The bolts are then identified as markers for dislocation measurement, and a method for bolt area of interest (AOI) positioning and pairing is introduced. This method calculates dislocation value through fitting reference surfaces from AOI. A series of experiments conducted on circular and quasi-rectangular shield tunnels totaling 2.45 km and containing 20 billion points demonstrate that the proposed segmentation workflow, coupled with an image semantic segmentation model, achieves a maximum mean intersection over union (mIoU) of 90.2%, which is 10% higher than directly segmenting tunnel point clouds. The proposed method went through repeated accuracy tests and was compared with other methods, including total station measurement, RANSAC plane fitting, and scanline method. The results of our proposed method demonstrate high precision and stability, with a measurement accuracy RMSE of 0.89 mm, an MAE of 0.69 mm, and an efficiency of 20 times higher compared to total station measurement.