螺栓孔特征点云支持下的地铁盾构隧道环缝识别与环间错台量分析

管片错台是地铁盾构隧道全生命周期中常见的一种隧道病害。错台量过大，可能会使成型的隧道发生大面积的破损和渗漏，对地铁隧道的耐久性和安全性产生极大的威胁。基于三维激光扫描技术的错台检测通过确定环缝位置提取环缝两侧的断面，继而分析错台量。目前的环缝位置识别需要将点云数据转化为正射影像，再采用人工标注的方法，该方法效率低，精度极易受作业人员的影响。本文提出了一种基于螺栓孔特征点云的地铁盾构隧道环缝识别方法，无需生成正射影像，充分利用盾构隧道既有特征点云，能够准确、快速地识别环缝位置，并在此基础上结合杭州市实际地铁工程进行环间错台检测以验证该方法。结果表明，相较于传统的隧道检测手段，该方法效率高，检测全面且监测结果能够达到工程应用的精度要求。

Circumferential seam detection and analytics of segment misplacement between rings of subway shield tunnels based on featured point cloud of bolt holes

Segment misplacement is a common disease that occurs frequently in the whole life cycle of subway shield tunnels. Excessive misalignment may cause huge damages and leakage to the tunnel, which poses a great threat to the durability and safety of the subway tunnel. Existing method uses 3D laser scanning technology to extract cross-sections on both sides of the circumferential seam by determining the position of the circumferential seam for misalignment detection. Unfortunately, existing method still needs to convert the point cloud data into orthophoto images, and then label the image manually. Using such a method is inefficient and the accuracy could be easily affected by the operator. This study proposes a method for detecting circumferential seams of subway shield tunnels based on the featured point cloud of bolt holes. The proposed method make full use of the collected featured point clouds of shield tunnels without generating orthophoto images for detecting the position of circumferential seams accurately and quickly. The authors uses a subway project in Hangzhou as a case study to validate the proposed method. The results show that, compared with the traditional method, the proposed method is highly efficient and satisfies the accuracy requirements of engineering applications.