Automatic Identification of Rock Discontinuity Sets by a Fuzzy C-Means Clustering Method Based on Artificial Bee Colony Algorithm
Source abstract
Abstract
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.
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Research context
OpenAlex topics: Drilling and Well Engineering · Image Processing and 3D Reconstruction · Mineral Processing and Grinding
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Key Laboratory of Ministry of Education for Efficient Mining and Safety of Metal Mines, University of Science and Technology Beijing, Beijing 100083, China
State Key Laboratory of Strata Intelligent Control and Green Mining Co-Founded by Shandong Province and The Ministry of Science and Technology, Shandong University of Science and Technology, Qingdao 266590, China
Key Laboratory of Intelligent Bionic Unmanned Systems of Ministry of Education, University of Science and Technology Beijing, Beijing 100083, China
Funding
Science, Technology & Innovation Project of Xiongan New Area · Open Research Fund of the State Key Laboratory of Strata Intelligent Control and Green Mining co-founded by Shandong Province and the Ministry of Science and Technology, Shandong University of Science and Technology · National Natural Science Foundation of China · Interdisciplinary Research Project for Young Teachers of USTB (Fundamental Research Funds for the Central Universities) · National Key R&D Program of China
References (39)
Show source-provided references
1. Analysis of Structural Homogeneity of Rock Masses10.1016/0013-7952(90)90050-B ↗
2. A Technique for Identifying Structural Domain Boundaries at the EKATI Diamond Mine10.1016/j.enggeo.2004.04.001 ↗
3. Experimental and Numerical Analyses of an Opening in a Jointed Rock Mass under Biaxial Compression10.1016/j.ijrmms.2011.09.001 ↗
4. Energy Evolution Mechanism and Failure Criteria of Jointed Surrounding Rock under Uniaxial Compression10.1007/s11771-021-4735-5 ↗
5. Macro-Meso Damage Cracking and Volumetric Dilatancy of Fault Block-in-Matrix Rocks Induced by Freeze–Thaw-Multistage Constant Amplitude Cyclic (F-T-MSCAC) Loads10.1111/ffe.13798 ↗
6. Investigation on Acoustic Emission Characteristics of Hole-Joint Contained Granite under a Compressive Disturbance: Experimental Insights10.2113/2022/3594940 ↗
7. Fuzzy C-Means Cluster Analysis Based on Genetic Algorithm for Automatic Identification of Joint Sets
8. Fracture Evolution and Failure Behavior around an Opening in Brittle Jointed Rocks Subjected to Uniaxial Compression10.1016/j.tafmec.2022.103651 ↗
9. Failure Behavior of the Surrounding Rock of Jointed Rock Masses in a Gold Mine under Blasting Impact Disturbance10.1007/s12665-021-10157-z ↗
10. Harrison, J.P. (1992, January 14–17). Fuzzy Objective Functions Applied to the Analysis of Discontinuity Orientation Data. Proceedings of the Rock Characterization: ISRM Symposium, Eurock ’92, Chester, UK.
11. Zhao, K., and Cai, M.F. (2002). Application of Fuzzy C-Means Cluster Algorithm in Identification of Structural Plane Group. Met. Mine, 13–15.10.1016/S1000-9361(11)60129-5 ↗
12. A Method for Multivariate Parameter Dominant Partitioning of Discontinuities of Rock Mass Based on Artificial Bee Colony Algorithm
13. Sources of Error in Joint Surveys10.1680/geot.1965.15.3.287 ↗
14. Estimating the Mean Trace Length of Rock Discontinuities10.1007/s006030050022 ↗
15. Delineation and Analysis of Clusters in Orientation Data10.1007/BF01039681 ↗
16. Mahtab, M.A., and Yegulalp, T.M. (1982, January 25–27). A Rejection Criterion for Definition of Clusters in Orientation Data. Proceedings of the The 23rd U.S. Symposium on Rock Mechanics (USRMS), Berkeley, CA, USA.
17. Fuzzy Cluster Algorithm for the Automatic Identification of Joint Sets10.1016/S0148-9062(98)00011-4 ↗
18. Dershowitz, W., Busse, R., Geier, J., and Uchida, M. (1996, January 19–21). A Stochastic Approach for Fracture Set Definition. Proceedings of the 2nd North America Rock Mechanics Symposium, Montreal, QC, Canada.
19. Synthetical Fuzzy Clustering Analysis for Joints Occurrence of Rock Mass
20. A Spectral Method for Clustering of Rock Discontinuity Sets10.1016/j.ijrmms.2006.02.003 ↗
21. Investigating the Validity of Conventional Joint Set Clustering Methods10.1016/j.enggeo.2011.01.002 ↗
22. Application of Artificial Neural Networks to Fracture Analysis at the Äspö HRL, Sweden: Fracture Sets Classification10.1016/S1365-1609(01)00030-2 ↗
23. Well-Separated Clusters and Optimal Fuzzy Partitions10.1080/01969727408546059 ↗
24. Bezdek, J.C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms. Advanced Applications in Pattern Recognition, Springer.10.1007/978-1-4757-0450-1 ↗
25. Optimal Choice of Weighting Exponent in a Fuzzy C Means Clustering Algorithm
26. Karaboğa, D. (2005). An Idea Based on Honey Bee Swarm for Numerical Optimization, Erciyes University. Technical Report-TR06.
27. A Powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm10.1007/s10898-007-9149-x ↗
28. On the Performance of Artificial Bee Colony (ABC) Algorithm10.1016/j.asoc.2007.05.007 ↗
29. A Novel Clustering Approach: Artificial Bee Colony (ABC) Algorithm10.1016/j.asoc.2009.12.025 ↗
30. Artificial Bee Colony Algorithm for Large-Scale Problems and Engineering Design Optimization10.1007/s10845-010-0393-4 ↗
31. An Upgraded Artificial Bee Colony (ABC) Algorithm for Constrained Optimization Problems10.1007/s10845-011-0621-6 ↗
32. Artificial Bee Colony Algorithm: A Survey
33. Unsupervised Optimal Fuzzy Clustering10.1109/34.192473 ↗
34. Multi-Parameter Dominant Grouping Method of Rock Mass Discontinuity Based on Principal Component Analysis
35. Crack Coalescence in Specimens with Open and Closed Flaws: A Comparison10.1016/j.ijrmms.2009.02.006 ↗
36. Crack Initiation, Propagation and Coalescence in Rock-like Material Containing Two Flaws: A Numerical Study Based on Bonded-Particle Model Approach10.1007/s00603-012-0323-1 ↗
37. Mechanical Responses and Fracturing Behavior of Jointed Rock Masses with a Cavity under Different Dynamic Loads10.1016/j.ijimpeng.2023.104608 ↗
38. Crack Initiation and Coalescence Behavior of Two Non-Parallel Flaws10.1007/s10706-017-0310-0 ↗
39. Correlation between the Rock Mass Properties and Maximum Horizontal Stress: A Case Study of Overcoring Stress Measurements10.1007/s12613-024-2944-2 ↗