Geomechanics and Engineering

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A hybrid algorithm for classifying rock joints based on improved artificial bee colony and fuzzy C-means clustering algorithm

  • Ji, Duofa (Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology) ;
  • Lei, Weidong (Shenzhen Graduate School, Harbin Institute of Technology) ;
  • Chen, Wenqin (Shenzhen Graduate School, Harbin Institute of Technology)
  • Received : 2019.07.02
  • Accepted : 2022.11.08
  • Published : 2022.11.25
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Abstract

This study presents a hybrid algorithm for classifying the rock joints, where the improved artificial bee colony (IABC) and the fuzzy C-means (FCM) clustering algorithms are incorporated to take advantage of the artificial bee colony (ABC) algorithm by tuning the FCM clustering algorithm to obtain the more reasonable and stable result. A coefficient is proposed to reduce the amount of blind random searches and speed up convergence, thus achieving the goals of optimizing and improving the ABC algorithm. The results from the IABC algorithm are used as initial parameters in FCM to avoid falling to the local optimum in the local search, thus obtaining stable classifying results. Two validity indices are adopted to verify the rationality and practicability of the IABC-FCM algorithm in classifying the rock joints, and the optimal amount of joint sets is obtained based on the two validity indices. Two illustrative examples, i.e., the simulated rock joints data and the field-survey rock joints data, are used in the verification to check the feasibility and practicability in rock engineering for the proposed algorithm. The results show that the IABC-FCM algorithm could be applicable in classifying the rock joint sets.

Keywords

Acknowledgement

The authors would like to acknowledge the financial support from research grants No.s 2019YFC1511105, 2021YFC3001002 by National Key R&D Program of China grant No. JCYJ20210324121402008 by Shenzhen Science and Technology Innovation Commission, grant No.s 52008142 and 51778193 by the National Natural Science Foundation of China, grant No. LH2020E057 by the Heilongjiang Provincial Natural Science Foundation of China.

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