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Rasouli, Ali, Esmaeilzadeh, Akbar, Mikaeil, Reza, Atalou, Solat. (1404). Optimizing Traditional Clustering Methods Using Metaheuristic Algorithms for Joint Set Identification in Copper Mines. پژوهشنامه مدیریت اجرایی, 2(3), 57-72. doi: 10.22080/cste.2025.29024.1032
Ali Rasouli; Akbar Esmaeilzadeh; Reza Mikaeil; Solat Atalou. "Optimizing Traditional Clustering Methods Using Metaheuristic Algorithms for Joint Set Identification in Copper Mines". پژوهشنامه مدیریت اجرایی, 2, 3, 1404, 57-72. doi: 10.22080/cste.2025.29024.1032
Rasouli, Ali, Esmaeilzadeh, Akbar, Mikaeil, Reza, Atalou, Solat. (1404). 'Optimizing Traditional Clustering Methods Using Metaheuristic Algorithms for Joint Set Identification in Copper Mines', پژوهشنامه مدیریت اجرایی, 2(3), pp. 57-72. doi: 10.22080/cste.2025.29024.1032
Rasouli, Ali, Esmaeilzadeh, Akbar, Mikaeil, Reza, Atalou, Solat. Optimizing Traditional Clustering Methods Using Metaheuristic Algorithms for Joint Set Identification in Copper Mines. پژوهشنامه مدیریت اجرایی, 1404; 2(3): 57-72. doi: 10.22080/cste.2025.29024.1032

Optimizing Traditional Clustering Methods Using Metaheuristic Algorithms for Joint Set Identification in Copper Mines

Contributions of Science and Technology for Engineering
دوره 2، شماره 3، مهر 2025، صفحه 57-72    اصل مقاله (1.91 M)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22080/cste.2025.29024.1032
نویسندگان
Ali Rasouli1؛ Akbar Esmaeilzadeh* 2؛ Reza Mikaeil2؛ Solat Atalou1
1Department of Mining Engineering, Ahar Branch, Islamic Azad University, Ahar, 54511-16714, Iran
2Department of Mining Engineering, Environment Faculty, Urmia University of Technology, Urmia, 57166-17165, Iran
تاریخ دریافت: 26 فروردین 1404،  تاریخ بازنگری: 20 اردیبهشت 1404،  تاریخ پذیرش: 20 خرداد 1404 
چکیده
Clustering assessment is crucial for identifying the primary characteristics of joints in mining and rock engineering. Orientation is commonly used to characterize the deformation patterns and mechanical properties of rock formations. This study introduces an enhanced clustering method by integrating the harmony search (HS) algorithm and the particle swarm optimization (PSO) algorithm to classify joint sets based on orientation parameters—namely, dip and dip direction—in the Sungun copper mine. First, the joint characteristics were clustered using K-means and fuzzy clustering techniques. The elbow method was applied to determine the optimal number of clusters, ultimately selecting a four-cluster classification. Subsequently, both K-means and fuzzy C-means (FCM) were optimized using HS and PSO algorithms, and the joint data were clustered and evaluated based on three clustering quality assessment criteria. The results demonstrated that the FCM-PSO method achieved the highest ranking among all tested methods, yielding a Davis-Bouldin index of 0.80, a Calinski-Harabasz index of 348.47, and a Silhouette score of 0.565. In contrast, integrating the HS algorithm with K-means and FCM did not enhance clustering performance as expected. Furthermore, the K-means-PSO method exhibited inferior performance compared to the FCM clustering approach, ranking third overall. Based on these findings, the FCM-PSO clustering method, by effectively determining optimal cluster centers, provides a reliable approach for classifying joint sets. The obtained results can be effectively utilized in rock mass behavior analysis for large-scale open-pit mines such as the Sungun copper mine.
کلیدواژه‌ها
Joint Set؛ Clustering Method؛ K-means and C-means Mmethod؛ Harmony Search Algorithm؛ PSO Algorithm؛ Sungun Copper Mine
مراجع
  1. Hu, J., Zhou, T., Ma, S., Yang, D., Guo, M., & Huang, P. (2022). Rock mass classification prediction model using heuristic algorithms and support vector machines: a case study of Chambishi copper mine. Scientific Reports, 12(1), 928. doi:10.1038/s41598-022-05027-y.
  2. Yang, B., Zhao, W., & Duan, Y. (2022). Critical damage threshold of brittle rock failure based on Renormalization Group theory. Geomechanics and Geophysics for Geo-Energy and Geo-Resources, 8(5), 135. doi:10.1007/s40948-022-00441-y.
  3. Cui, X., & Yan, E. chuan. (2020). Fuzzy C-Means Cluster Analysis Based on Variable Length String Genetic Algorithm for the Grouping of Rock Discontinuity Sets. KSCE Journal of Civil Engineering, 24(11), 3237–3246. doi:10.1007/s12205-020-2188-2.
  4. Xu, L. M., Chen, J. P., Wang, Q., & Zhou, F. J. (2013). Fuzzy C-means cluster analysis based on mutative scale chaos optimization algorithm for the grouping of discontinuity sets. Rock Mechanics and Rock Engineering, 46(1), 189–198. doi:10.1007/s00603-012-0244-z.
  5. Ye, J. (2017). A netting method for clustering-simplified neutrosophic information. Soft Computing, 21(24), 7571–7577. doi:10.1007/s00500-016-2310-z.
  6. Shanley, R. J., & Mahtab, M. A. (1976). Delineation and analysis of clusters in orientation data. Journal of the International Association for Mathematical Geology, 8(1), 9–23. doi:10.1007/BF01039681.
  7. Tokhmechi, B., Memarian, H., Moshiri, B., Rasouli, V., & Noubari, H. A. (2011). Investigating the validity of conventional joint set clustering methods. Engineering Geology, 118(3–4), 75–81. doi:10.1016/j.enggeo.2011.01.002.
  8. Zhou, W., & Maerz, N. H. (2002). Implementation of multivariate clustering methods for characterizing discontinuities data from scanlines and oriented boreholes. Computers & Geosciences, 28(7), 827–839. doi:10.1016/S0098-3004(01)00111-X.
  9. Hammah, R. E., & Curran, J. H. (1999). On distance measures for the fuzzy K-means algorithm for joint data. Rock Mechanics and Rock Engineering, 32(1), 1–27. doi:10.1007/s006030050041.
  10. Hammah, R. E., & Curran, J. H. (2000). Validity Measures for the Fuzzy Cluster Analysis of Orientations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(12), 1467–1472. doi:10.1109/34.895981.
  11. Jimenez, R. (2008). Fuzzy spectral clustering for identification of rock discontinuity sets. Rock Mechanics and Rock Engineering, 41(6), 929–939. doi:10.1007/s00603-007-0155-6.
  12. Jimenez-Rodriguez, R., & Sitar, N. (2006). A spectral method for clustering of rock discontinuity sets. International Journal of Rock Mechanics and Mining Sciences, 43(7), 1052–1061. doi:10.1016/j.ijrmms.2006.02.003.
  13. Liu, J., Zhao, X. D., & Xu, Z. he. (2017). Identification of rock discontinuity sets based on a modified affinity propagation algorithm. International Journal of Rock Mechanics and Mining Sciences, 94, 32–42. doi:10.1016/j.ijrmms.2017.02.012.
  14. Li, Y., Wang, Q., Chen, J., Xu, L., & Song, S. (2015). K-means Algorithm Based on Particle Swarm Optimization for the Identification of Rock Discontinuity Sets. Rock Mechanics and Rock Engineering, 48(1), 375–385. doi:10.1007/s00603-014-0569-x.
  15. Ma, G. W., Xu, Z. H., Zhang, W., & Li, S. C. (2015). An enriched K-means clustering method for grouping fractures with meliorated initial centers. Arabian Journal of Geosciences, 8(4), 1881–1893. doi:10.1007/s12517-014-1379-x.
  16. Song, S., Wang, Q., Chen, J., Li, Y., Zhang, W., & Ruan, Y. (2017). Fuzzy C-means clustering analysis based on quantum particle swarm optimization algorithm for the grouping of rock discontinuity sets. KSCE Journal of Civil Engineering, 21(4), 1115–1122. doi:10.1007/s12205-016-1223-9.
  17. Song, T., Chen, J., Zhang, W., Xiang, L. J., & Yang, J. H. (2015). A method for multivariate parameter dominant partitioning of discontinuities of rock mass based on artificial bee colony algorithm. Rock Soil Mech 36: 861–868.
  18. Ding, Q., Huang, R., Wang, F., Chen, J., Wang, M., & Zhang, X. (2018). Multi-Parameter Dominant Grouping of Discontinuities in Rock Mass Using Improved ISODATA Algorithm. Mathematical Problems in Engineering, 2018(1), 5619404. doi:10.1155/2018/5619404.
  19. Shirazy, A., Hezarkhani, A., Shirazi, A., Khakmardan, S., & Rooki, R. (2021). K-Means Clustering and General Regression Neural Network Methods for Copper Mineralization probability in Char-Farsakh, Iran. Türkiye Jeoloji Bülteni / Geological Bulletin of Turkey, 65(1), 79–92. doi:10.25288/tjb.1010636.
  20. Mikaeil, R., Haghshenas, S. S., Haghshenas, S. S., & Ataei, M. (2018). Performance prediction of circular saw machine using imperialist competitive algorithm and fuzzy clustering technique. Neural Computing and Applications, 29(6), 283–292. doi:10.1007/s00521-016-2557-4.
  21. Esmaeilzadeh, A., & Shahriar, K. (2019). Optimized fuzzy cmeans – fuzzy covariance – fuzzy maximum likelihood estimation clustering method based on deferential evolutionary optimization algorithm for identification of rock mass discontinuities sets. Periodica Polytechnica Civil Engineering, 63(2), 674–686. doi:10.3311/PPci.13885.
  22. Zheng, S., Jiang, A. N., Yang, X. R., & Luo, G. C. (2020). A New Reliability Rock Mass Classification Method Based on Least Squares Support Vector Machine Optimized by Bacterial Foraging Optimization Algorithm. Advances in Civil Engineering, 2020(1), 3897215. doi:10.1155/2020/3897215.
  23. Ruan, Y., Chen, J., Fan, Z., Wang, T., Mu, J., Huo, R., Huang, W., Liu, W., Li, Y., & Sun, Y. (2023). Application of K-PSO Clustering Algorithm and Game Theory in Rock Mass Quality Evaluation of Maji Hydropower Station. Applied Sciences (Switzerland), 13(14), 8467. doi:10.3390/app13148467.
  24. Ruan, Y., Liu, W., Wang, T., Chen, J., Zhou, X., & Sun, Y. (2023). Dominant Partitioning of Discontinuities of Rock Masses Based on DBSCAN Algorithm. Applied Sciences (Switzerland), 13(15), 8917. doi:10.3390/app13158917.
  25. Wang, R., Ni, Y., Zhang, L., & Gao, B. (2025). Grouped machine learning methods for predicting rock mass parameters in a tunnel boring machine-driven tunnel based on fuzzy C-means clustering. Deep Underground Science and Engineering, 4(1), 55–71. doi:10.1002/dug2.12082.
  26. Yong, R., Wang, H., Ye, J., Du, S., & Luo, Z. (2024). Neutrosophic genetic algorithm and its application in clustering analysis of rock discontinuity sets. Expert Systems with Applications, 245, 122973. doi:10.1016/j.eswa.2023.122973.
  27. Zarean, A., & Poormirzaee, R. (2016). Joint inversion of ReMi dispersion curves and refraction travel times using particle swarm optimization algorithm. In Journal of Mining and Environment 7(1), 67–79.
  28. Hezarkhani, A., & Williams-Jones, A. E. (1998). Controls of alteration and mineralization in the Sungun porphyry copper deposit, Iran: evidence from fluid inclusions and stable isotopes. Economic Geology, 93(5), 651–670. doi:10.2113/gsecongeo.93.5.651.
  29. Calagari, A. A. (1997). Geochemical, stable isotope, noble gas and fluid inclusion studies of mineralization and alteration at Sungun porphyry copper deposit, East Azarbaidjan, Iran: Implications for genesis. PhD Thesis, The University of Manchester, Manchester, United Kingdom.
  30. Edwards, A. C. (Ed.). (2001). Mineral resource and ore reserve estimation - the AusIMM guide to good practice (monograph 23). Minerals Engineering, 14(9), II. doi:10.1016/s0892-6875(01)80033-9.
  31. Bagheryan, A. (2006). Copper concentration process in the Sungun copper complex (Internal report). National Iranian Copper Industries Company, Tabriz, Iran.
  32. Aggarwal, V., Gupta, V., Singh, P., Sharma, K., & Sharma, N. (2019). Detection of spatial outlier by using improved Z-score test. Proceedings of the International Conference on Trends in Electronics and Informatics, ICOEI 2019, 2019-April, 788–790. doi:10.1109/icoei.2019.8862582.
  33. Wong, L. N. Y., & Liu, G. (2010). An improved K-means clustering method for the automatic grouping of discontinuity sets. 44th US Rock Mechanics Symposium - 5th US/Canada Rock Mechanics Symposium, 10, 27 June, 2010, Salt Lake City, United States.
  34. Ketchen, D. J., & Shook, C. L. (1996). The application of cluster analysis in strategic management research: An analysis and critique. Strategic Management Journal, 17(6), 441–458. doi:10.1002/(sici)1097-0266(199606)17:6<441::aid-smj819>3.0.co;2-g.
  35. Adolfsson, A., Ackerman, M., & Brownstein, N. C. (2016). To cluster, or not to cluster: How to answer the estion. TKDD, 17, 1-9.
  36. MacQueen, J. (1967). Multivariate observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistic sand Probability, 1, 281-297, Volume 1: Statistics, University of California Press, Berkeley, United States.
  37. Dunn, J. C. (1974). Well-separated clusters and optimal fuzzy partitions. Journal of Cybernetics, 4(1), 95–104. doi:10.1080/01969727408546059.
  38. Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2–3), 191–203. doi:10.1016/0098-3004(84)90020-7.
  39. Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A New Heuristic Optimization Algorithm: Harmony Search. Simulation, 76(2), 60–68. doi:10.1177/003754970107600201.
  40. Wang, X., Gao, X.-Z., & Zenger, K. (2015). An Introduction to Harmony Search Optimization Method. Springer, Cham, Switzerland. doi:10.1007/978-3-319-08356-8.
  41. Haghshenas, S. S., Haghshenas, S. S., Geem, Z. W., Kim, T. H., Mikaeil, R., Pugliese, L., & Troncone, A. (2021). Application of harmony search algorithm to slope stability analysis. Land, 10(11). doi:10.3390/land10111250.
  42. Kennedy, J. and Eberhart, R. (1995) Particle swarm optimization. Proceedings of ICNN’95 - International Conference on Neural Networks, 4, 1942–1948. doi:10.1109/icnn.1995.488968.
  43. Kennedy, J. (1998). The behavior of particles. Evolutionary Programming VII. EP 1998. Lecture Notes in Computer Science, vol 1447. Springer, Berlin, Germany. doi:10.1007/BFb0040809.
  44. Shen, J., Wang, C., Wang, R., Huang, F., Fan, C., & Xu, L. (2015). A Band Selection Method for Hyperspectral Image Classification based on improved Particle Swarm Optimization. International Journal of Signal Processing, Image Processing and Pattern Recognition, 8(4), 325–338. doi:10.14257/ijsip.2015.8.4.28.
  45. Gao, H., Yang, Y., Zhang, X., Li, C., Yang, Q., & Wang, Y. (2019). Dimension reduction for hyperspectral remote sensor data based on multi-objective particle swarm optimization algorithm and game theory. Sensors (Switzerland), 19(6). doi:10.3390/s19061327.
  46. Caliñski, T., & Harabasz, J. (1974). A Dendrite Method Foe Cluster Analysis. Communications in Statistics, 3(1), 1–27. doi:10.1080/03610927408827101.
  47. Davies, D. L., & Bouldin, D. W. (1979). A Cluster Separation Measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1(2), 224–227. doi:10.1109/TPAMI.1979.4766909.
  48. Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65. doi:10.1016/0377-0427(87)90125-7
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