摘要 为提高岩体结构面信息获取精度,基于结构面三维点云数据,改进DBSCAN(density-based spatial clustering of applications with noise)密度聚类算法。利用k近邻算法与基于密度比S的评价准则划分出不同密度点云区域以自适应设置参数ε并分析点云法向量,引入法向量夹角阈值T判断属于同组结构面的点集并用相同颜色显示。本研究讨论了参数最优化组合对识别结果的影响,实现优势结构面与产状赤平投影的快速分析。研究结果能够为结构面信息的智能化高效测量提供一种可靠的应用方法。
Abstract: To improve the accuracy of information acquisition, the density-based spatial clustering application with noise(DBSCAN)algorithm was improved based on 3D point clouds. The k-nearest neighbor algorithm and the evaluation criterion based on density ratio S were used to divide the point cloud regions with different densities in order to set parameter ε and analyze the point cloud normal vector adaptive. The angle threshold T of normal vectors was introduced to determine the points belonging to the same plane and the points belonging to the same plane were displayed with the same colour. This paper discussed the influence of different parameter combinations on the identification results and enabled a fast analysis of rock joints. The research results could provide a reliable application method for efficient measurement of discontinuity information.
李胜, 熊自明, 刘一鸣, 李志浩. 基于改进DBSCAN算法的岩体结构面智能识别方法[J]. 隧道与地下工程灾害防治, 2022, 4(2): 49-58.
LI Sheng, XIONG Ziming, LIU Yiming, LI Zhihao. Intelligent identification of rock discontinuities based on an improved DBSCAN algorithm. Hazard Control in Tunnelling and Underground Engineering, 2022, 4(2): 49-58.
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2022, Vol. 4 
