Detection of Rock Joint Sets using Optimized Fuzzy Clustering by Particle Swarm Algorithm (Case Study: Sungun Copper Mine) | ||
| Journal of Mining and Environment | ||
| مقاله 18، دوره 17، شماره 3، مرداد و شهریور 2026، صفحه 1089-1107 اصل مقاله (6.27 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2025.15784.3039 | ||
| نویسندگان | ||
| Ali Rasouli1؛ Akbar Esmaeilzadeh2؛ Reza Mikaeil* 2؛ Solat Atalou1 | ||
| 1Department of Mining Engineering, Ahar Branch, Islamic Azad University, Ahar, Iran | ||
| 2Department of Mining Engineering, Faculty of Environment, Urmia University of Technology, Urmia, Iran | ||
| چکیده | ||
| Identifying joint sets is essential in engineering geology for rock mass classification and slope stability analysis in mining. Accurate clustering of joint sets based on dip and dip direction enhances the understanding of rock behavior and ensures stability in mine walls. This study presents a novel clustering approach integrating the Harmony Search (HS) and Particle Swarm Optimization (PSO) algorithms to classify joint sets in the Sungun copper mine. Initially, joint characteristics were classified using the Fuzzy C-Means (FCM) method, with the elbow method selecting a four-class clustering solution. To optimize clustering, FCM was combined with HS and PSO, and joint data were assessed using Davies-Bouldin, Calinski–Harabasz, and Silhouette indices. The results demonstrated that the hybrid FCM-PSO method outperformed alternatives, achieving scores of 0.80, 347.48, and 0.57, respectively, indicating superior clustering performance and stability. In contrast, the FCM-HS method performed worse than FCM alone, ranking third overall. The findings confirm that FCM-PSO effectively classifies joint sets, providing reliable insights into rock mass behavior in the Sungun mine. Considering the features and advantages of the FCM-PSO method, it is concluded that the proposed approach has significant potential for effective joint classification in mining engineering. This improved clustering approach enhances geological analysis, supporting safer and more efficient mining operations. | ||
| کلیدواژهها | ||
| Clustering method؛ Joint set؛ Fuzzy method؛ Particle swarm optimization algorithm؛ Songun copper mine | ||
| مراجع | ||
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