Automatic Identification of Rock Discontinuity Sets by a Fuzzy C-Means Clustering Method Based on Artificial Bee Colony Algorithm
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/3/1497 |
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| author | Peng Li Tianqi Chen Yan Liu Meifeng Cai Liang Sun Peitao Wang Yu Wang Xuepeng Zhang |
| author_facet | Peng Li Tianqi Chen Yan Liu Meifeng Cai Liang Sun Peitao Wang Yu Wang Xuepeng Zhang |
| author_sort | Peng Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ce8a6cbc359b4bb09e70613e1e0406f1 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-ce8a6cbc359b4bb09e70613e1e0406f12025-08-20T02:12:24ZengMDPI AGApplied Sciences2076-34172025-02-01153149710.3390/app15031497Automatic Identification of Rock Discontinuity Sets by a Fuzzy C-Means Clustering Method Based on Artificial Bee Colony AlgorithmPeng Li0Tianqi Chen1Yan Liu2Meifeng Cai3Liang Sun4Peitao Wang5Yu Wang6Xuepeng Zhang7Key Laboratory of Ministry of Education for Efficient Mining and Safety of Metal Mines, University of Science and Technology Beijing, Beijing 100083, ChinaKey Laboratory of Intelligent Bionic Unmanned Systems of Ministry of Education, University of Science and Technology Beijing, Beijing 100083, ChinaKey Laboratory of Ministry of Education for Efficient Mining and Safety of Metal Mines, University of Science and Technology Beijing, Beijing 100083, ChinaKey Laboratory of Ministry of Education for Efficient Mining and Safety of Metal Mines, University of Science and Technology Beijing, Beijing 100083, ChinaKey Laboratory of Intelligent Bionic Unmanned Systems of Ministry of Education, University of Science and Technology Beijing, Beijing 100083, ChinaKey Laboratory of Ministry of Education for Efficient Mining and Safety of Metal Mines, University of Science and Technology Beijing, Beijing 100083, ChinaKey Laboratory of Ministry of Education for Efficient Mining and Safety of Metal Mines, University of Science and Technology Beijing, Beijing 100083, ChinaState 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, ChinaThe 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.https://www.mdpi.com/2076-3417/15/3/1497rock discontinuity setsstructural plane investigationFCM-ABC methodautomatic identification |
| spellingShingle | Peng Li Tianqi Chen Yan Liu Meifeng Cai Liang Sun Peitao Wang Yu Wang Xuepeng Zhang Automatic Identification of Rock Discontinuity Sets by a Fuzzy C-Means Clustering Method Based on Artificial Bee Colony Algorithm Applied Sciences rock discontinuity sets structural plane investigation FCM-ABC method automatic identification |
| title | Automatic Identification of Rock Discontinuity Sets by a Fuzzy C-Means Clustering Method Based on Artificial Bee Colony Algorithm |
| title_full | Automatic Identification of Rock Discontinuity Sets by a Fuzzy C-Means Clustering Method Based on Artificial Bee Colony Algorithm |
| title_fullStr | Automatic Identification of Rock Discontinuity Sets by a Fuzzy C-Means Clustering Method Based on Artificial Bee Colony Algorithm |
| title_full_unstemmed | Automatic Identification of Rock Discontinuity Sets by a Fuzzy C-Means Clustering Method Based on Artificial Bee Colony Algorithm |
| title_short | Automatic Identification of Rock Discontinuity Sets by a Fuzzy C-Means Clustering Method Based on Artificial Bee Colony Algorithm |
| title_sort | automatic identification of rock discontinuity sets by a fuzzy c means clustering method based on artificial bee colony algorithm |
| topic | rock discontinuity sets structural plane investigation FCM-ABC method automatic identification |
| url | https://www.mdpi.com/2076-3417/15/3/1497 |
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