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