Automatic Identification of Rock Discontinuity Sets by a Fuzzy C-Means Clustering Method Based on Artificial Bee Colony Algorithm

作     者：Li, Peng Chen, Tianqi Liu, Yan Cai, Meifeng Sun, Liang Wang, Peitao Wang, Yu Zhang, Xuepeng 

作者机构：Univ Sci & Technol Beijing Key Lab Minist Educ Efficient Min & Safety Met Min Beijing 100083 Peoples R China Shandong Univ Sci & Technol State Key Lab Strata Intelligent Control & Green M Qingdao 266590 Peoples R China Shandong Univ Sci & Technol Minist Sci & Technol Qingdao 266590 Peoples R China Univ Sci & Technol Beijing Key Lab Intelligent Bion Unmanned Syst Minist Educ Beijing 100083 Peoples R China 

出 版 物：《APPLIED SCIENCES-BASEL》 (Appl. Sci.)

年 卷 期：2025年第15卷第3期

页      面：1497-1497页

核心收录：

基　　金：Science, Technology & Innovation Project of Xiongan New Area Open Research Fund of the State Key Laboratory of Strata Intelligent Control and Green Mining Ministry of Science and Technology, Shandong University of Science and Technology [SICGM202303] National Natural Science Foundation of China [52204084, 52474091] Interdisciplinary Research Project for Young Teachers of USTB (Fundamental Research Funds for the Central Universities) [FRF-IDRY-GD22-002] National Key R&D Program of China [2022YFC2905600, 2022YFC3004601] 2023XAGG0061 

主　　题：rock discontinuity sets structural plane investigation FCM-ABC method automatic identification 

摘      要：The identification and classification of rock discontinuities are crucial for studying rock mechanical properties and rock engineering optimization design and safety assessment. An improved artificial bee colony (ABC) algorithm is proposed and combined with the fuzzy C-means (FCM) clustering method to develop an FCM clustering method for automatically identifying rock discontinuity sets based on the ABC algorithm (FCM-ABC method). All the equations of the method are fully developed, and the methodology is presented in its entirety. Moreover, the rock structural planes are investigated in a gold mine in China using a ShapeMetriX 3D system. Based on the measured structural plane data, the specific calculation process, selection of parameters, effectiveness of grouping, and the dominant orientation of the proposed method for structural plane occurrence classification are analyzed and discussed, and satisfactory clustering results are achieved. This validates the validity and reliability of the method. Furthermore, multiple aspects of the excellent performance of this method for the identification of structural plane sets compared to traditional clustering methods are demonstrated. In addition, the significance of structural plane identification in the prevention and control of rock engineering disasters is discussed. This new method theoretically expands the technology of rock mass structural plane identification and has important application value in practical engineering.