Automatic identification of irregular rock blocks from 3D point cloud data of rock surface. Issue 2 (October 2021)

• Record Type: Journal Article
• Authors: Long, Yangjun; Huang, Qian; Wu, Faquan; Yi, Hongqing; Guan, Shenggong; Sha, Peng
• Is Part Of: IOP conference series. Volume 861:Issue 2(2021)
• Publication Date: 2021-10
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/861/2/022048
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: The block size is an important index for determining the integrity of rock masses, as well as the evidence for estimating the rock mass classification. However, obtaining the block size information of exposed rock is a time-consuming, labor-intensive and high risk work. This paper proposed a new method to automatically identify the rock block and calculate the block size based on the 3D point cloud data of rock surface. The proposed method includes four steps: (1) automatic identification of rock discontinuity and extraction of information based on several machine learning algorithms, (2) Rough extraction of block discontinuities combination by distance filtering and angle filtering, (3) accurate identification and extraction of blocks by applying mutual projection of centroid and adding angle repair coefficient, (4) calculation of block size by filling in the concavity outline of point cloud for single block. An automatically statistic and analyzed program is compiled by matlab language based on massive point cloud of block information. The method is applied in controllable boxes model and real road cut slope, making comparison with the results of former research and calculated results by specialized point cloud processing software. It reveals that the proposed method is applicable with high calculating accuracy and efficiency.