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隧道建设（中英文） ›› 2026, Vol. 46 ›› Issue (S1): 196-204.DOI: 10.3973/j.issn.2096-4498.2026.S1.017

• 研究与探索 • 上一篇    下一篇

基于双目视觉点云的泥水盾构筛分岩渣体积快速监测方法

蒋华1, 王子潇2, 郭永建1, 干聪豫3, 刘泓志3, 孙树良3   

  1. （1. 青岛国信胶州湾第二海底隧道有限公司， 山东 青岛 266031； 2. 北京交通大学土木建筑工程学院， 北京 100044； 3. 中交隧道工程局有限公司， 山东 青岛 100102）
  • 出版日期:2026-06-30 发布日期:2026-07-23
  • 作者简介:蒋华（1989—），男，吉林桦甸人，2020年毕业于北京科技大学，土木工程专业，博士，高级工程师，主要从事隧道与地下工程的研究与建设管理工作。 E-mail: jhuazi1989@163.com。

Intelligent Monitoring of Rock Slag Volume in Slurry Shield Screening Based on Binocular-Vision Point Cloud

JIANG Hua1, WANG Zixiao2, GUO Yongjian1, GAN Congyu3, LIU Hongzhi3, SUN Shuliang3   

  1. (1. Qingdao Guoxin Jiaozhou Bay Second Submarine Tunnel Co., Ltd., Qingdao 266031, Shandong, China; 2. School of Civil Engineering and Architecture, Beijing Jiaotong University, Beijing 100044, China; 3. CCCC Tunnel Engineering Co., Ltd., Qingdao 100102, Shandong, China)
  • Online:2026-06-30 Published:2026-07-23

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摘要： 针对目前泥水盾构筛分岩渣体积监测分析难度高、准确度难以保证和依赖现场人员经验等问题，提出一种基于双目视觉点云的泥水盾构筛分岩渣体积智能监测技术。依托青岛胶州湾第二海底隧道盾构区间，设置多组试验工况，使用双目景深相机获取堆积岩渣目标的点云数据，并对点云模型进行处理计算： 基于RANSAC算法进行点云平面检测与剔除，初步获得较为精准的点云数据，通过姿态变换使点云数据匹配选定的地面坐标系； 基于ICP算法进行点云配准，以此获得横向堆积多土堆模型； 基于高斯滤波的统计学方法进行离散点降噪； 基于Delaunay算法将点云数据三角剖分后进行体积计算，验证分析算法的准确性。结果表明，筛分岩渣体积监测算法在合理的参数配置下能够达到较高的准确率，试验结果平均准确率高于95%，每50环监测平均相对误差稳定在3.94%~5.38%，最小相对误差仅0.86%。

关键词: 泥水盾构, 双目视觉, 点云, 筛分岩渣体积, 智能监测

Abstract: The volume of screened rock slag from slurry shield tunneling is a critical parameter reflecting the overbreak and underbreak conditions of slurry shield tunneling face. It is hard to monitor and analyze with unstable measurement accuracy, and heavily relies on personnel’s experience. To address these challenges, an intelligent monitoring method based on binocular-vision point cloud is proposed for intelligent monitoring of volume of screened rock slag from slurry shield tunneling. Based on a case study of shield tunneling section of the Qingdao Second Jiaozhou Bay Subsea Tunnel, multiple groups of field test conditions are designed, a binocular depth camera is used to collect point cloud data of the accumulated rock slag target, and the point cloud model is systematically processed and calculated. These processes include: (1) The random sample consensus algorithm is applied to detect and eliminate ground plane point clouds, obtaining preliminarily refined target point cloud data. (2) Pose transformation is performed to match the point cloud data with the selected ground reference coordinate system. (3) The iterative closest point algorithm is adopted for point cloud registration to construct a complete model of multiple horizontally accumulated rock slag. (4) The Gaussian filtering combined with a statistical outlier removal method is used to denoise discrete noise points. (5) The volume of the rock slag accumulation is calculated through Delaunay triangulation of the processed point cloud, and the accuracy of the proposed algorithm is verified and analyzed. The test results show that the proposed monitoring algorithm for calculating screened rock slag volume achieves high accuracy under rational parameter configurations. The average accuracy of the test results is higher than 95%, the average relative monitoring error for every 50 rings is stable in the range of 3.94% to 5.38%, and the minimum relative error is only 0.86%.

Key words: slurry shield, binocular vision, point cloud, screened rock slag volume, intelligent monitoring

主办单位：中铁隧道勘察设计研究院有限公司    主管单位：中铁隧道局集团有限公司

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