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| Intelligent identification of rock discontinuities based on an improved DBSCAN algorithm |
| LI Sheng, XIONG Ziming*, LIU Yiming, LI Zhihao
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| State Key Laboratory of Disaster Prevention and Mitigation of Explosion and Impact, Army Engineering University of PLA, Nanjing 210007, Jiangsu, China |
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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.
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Received: 25 March 2022
Published: 20 June 2022
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