编号：CDUT-2024-38

标题：An efficient method for extracting and clustering rock mass discontinuities from 3D point clouds

入藏号：WOS:000921578100004

中国科学院文献情报中心期刊分区：工程技术1区/TOP（2023）

本校作者：易小宇; 冯文凯; 王迪; 杨荣浩; 胡云鹏; 周永健

来源出版物：ACTA GEOTECHNICA  卷: 18  期: 7  页: 3485-3503      

出版年：2023

第一地址：成都理工大学

关键词：3D point cloud; Region growing algorithm; Rock mass discontinuity; K-means clustering algorith

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摘要：A rock mass discontinuity is a fundamental element of a rock mass structure that regulates its mechanical and hydrogeological properties and has significant engineering implications. In this research, we offer a method for extracting discontinuities and performing efficient clustering analysis based on 3D point cloud data for rock outcrops. First, the K-d tree approach is utilized to organize the point cloud data so that the normal vector and curvature can be calculated quickly. Discontinuities are then extracted using a multirule region growing algorithm, and the dip directions and dip angles of the discontinuities are calculated. Then, the improved farthest point sampling algorithm and the elbow method are used to optimize the K-means algorithm and finally automatically determine the main discontinuity set and average direction for the rock mass. The approach is tested on two real cases and compared to the methods of international researchers, and it is discovered that the method proposed in this work shows good accuracy, with an average deviation of less than 5 degrees from the dip direction and dip angle. Comparative tests with many point cloud data sets show that this new method can be used to extract discontinuities from massive-scale rock outcrop point cloud data and perform cluster analysis with high efficiency. The proposed method gives geologists and geological engineers a new tool for quickly and efficiently understanding rock outcrop discontinuities.

文章链接地址: https://link.springer.com/article/10.1007/s11440-023-01803-w