Investigation on Relationship between Rock Characteristics and Blasting Fragmentation using Fractal Analysis | ||
| Journal of Mining and Environment | ||
| مقاله 16، دوره 17، شماره 1، فروردین و اردیبهشت 2026، صفحه 309-320 اصل مقاله (7.05 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2024.15240.2917 | ||
| نویسندگان | ||
| Masoud Monjezi* 1؛ Morteza Baghestani1؛ Peyman Afzal2؛ Ali Reza Yarahmadi Bafghi3 | ||
| 1Faculty of Engineering, Tarbiat Modares University, Tehran, Iran | ||
| 2Department of Mining Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran | ||
| 3Department of Mining and Metallurgical Engineering, Yazd University, Yazd, Iran | ||
| چکیده | ||
| Blasting is an essential operation in mining projects, significantly affecting the particle-size distribution, which is critical for subsequent processes such as loading, hauling, and milling. Effectiveness of the blasting operations rely on accurate rock characterization, especially when dealing with different rock types. Proper rock and fragmentation characterization allows for tailored blast designs and also can lead to precise predictions of fragmentation quality. Various characterization techniques exist. This paper examines the application of fractal analysis to classify fragmentation quality and rock types, utilizing the Choghart iron mine in Iran as a case study. Extensive fieldwork collected data on rock properties (uniaxial compressive strength and density) and fragmentation outcomes during blasting. The fractal modeling revealed distinct breakpoints for classification, followed by Logratio analysis to assess relationships among the identified classes. Finally, mathematical models were established to predict fragmentation features based on the relevant rock attributes. The models demonstrated improved predictive accuracy as compared to the prior classifications. | ||
| کلیدواژهها | ||
| Keyword: Blasting operation؛ Fragmentation feature؛ Fractal analysis؛ Logratio matrix | ||
| مراجع | ||
|
[1]. Monjezi, M., Rezaei, M., & Yazdian Varjani, A. (2009). Prediction of rock fragmentation due to blasting in Gol-E-Gohar iron mine using fuzzy logic. International Journal of Rock Mechanics and Mining Sciences 46(8). 1273–1280.
[2]. Akbari, M., Lashkaripour, G., Bafghi, A.Y., & Ghafoori, M. (2015). Blastability evaluation for rock mass fragmentation in Iran central iron ore mines. International Journal of Mining Sciences and Technology 25(1) 59–66.
[3]. Sasaoka, T., Takahashi, Y., Sugeng, W., Hamanaka, A., Shimada, H., Matsui, K., & Kubota, S. (2015). Effects of Rock Mass Conditions and Blasting Standard on Fragmentation Size at Limestone Quarries. Open journal of geology 05(05) 331.
[4]. Singh, P.K., Roy, M.P., Paswan, R.K., Sarim, M.D., Kumar, S., 7 Ranjan, R. (2016). Rock fragmentation control in opencast blasting, Journal of Rock Mechanics and Geotechnical Engineering, 8 (2), 225-237.
[5]. MacKenzie, A.S. (1966). Cost of explosives—do you evaluate it properly? Mining Congress Journal 32–41.
[6]. Michaud, P., Lizotte, Y., & Scoble, M. (1997). Rock fragmentation and mining productivity: characterization and case studies. In: Proceedings of the 23rd annual conference on explosives and blasting technique, Las Vegas, NV, 61–72.
[7]. Sanchidrián, J., Segarra, P., & López, L. (2006). A practical procedure for the measurement of fragmentation by blasting by image analysis. Rock Mechanics and Rock Engineering, 39(4), 359–382.
[8]. Kulatilake, P.H.S.W., Hudaverdi, T., & Wu, Q. (2012). New prediction models for mean particle size in rock blast fragmentation. Geotechnical and Geological Engineering 30(3) 665–684.
[9]. Konya, C.J., & Walter, E.J. (1991). Rock blasting and overbreak control. United States Department of Transportation, McClean.
[10]. Latham, J.P., & Lu, P. (1999). Development of an assessment system for the blastability of rock masses. International Journal of Rock Mechanics and Mining Sciences 36(1):41–55.
[11]. Azimi, Y., Osanloo, M., Aakbarpour-Shirazi, M., & Aghajani Bazzazi, A. (2010). Prediction of the blastability designation of rock masses using fuzzy sets. International Journal of Rock Mechanics and Mining Sciences, 47(7), 1126–1140.
[12]. Kuznetsov, V.M. (1973). The mean diameter of fragments formed by blasting rock. Journal of Mining Sciences, 9, 144–148.
[13]. Hjelmberg, H. (1983). Some ideas on how to improve calculations of the fragment size distribution in bench blasting. 1st International Symposium on Rock Fragmentation by Blasting, 469–494.
[14]. Kou, S., & Rustan, A. (1993). Computerized design and result predictions of bench blasting. In: Rossmanith HP (ed) Proceedings of 4th international symposium on rock fragmentation by blasting (Fragblast 4), Vienna, Austria, Balkema, Rotterdam, pp 263–271.
[15]. Cunningham, C.V.B. (1983). The Kuz-Ram model for prediction of fragmentation from blasting. In: Proceedings of the 1st international symposium on rock fragmentation by blasting, Lulea, Sweden, 439–453.
[16]. Cunningham, C.V.B. (1987). Fragmentation estimations and the Kuz-Ram Model-Four years on. In: Proc. 2nd Int. Symposium on Rock Fragmentation by Blasting, 475–487
[17]. Cunningham, C.V.B., (2005) The Kuz–Ram fragmentation model—20 years on. In: Proceedings of the 3rd EFEE World Conference on Explosives and Blasting, England, 201–210.
[18]. Dahlhielm, S. (1996). Industrial applications of image analysis—the IPACS system. Measurement of Blast Fragmentation, first edition, Routledge.
[19]. Djordjevic, N. (1999). A two-component model of blast fragmentation. Australian Institute of Mining and Metallurgy Proceedings 304, 9–13.
[20]. Bahrami, A., Monjezi, M., Goshtasbi, K., & Ghazvinian, A. (2011). Prediction of rock fragmentation due to blasting using artificial neural network. Engineering with Computers 27(2) 177–181.
[21]. Hudaverdi, T., Kulatilake, P., & Kuzu, C. (2011). Prediction of blast fragmentation using multivariate analysis procedures. International Journal of Numerical and Analytical Methods Geomechanics 35(12) 1318–1333.
[22]. Shi, X.Z., Jian, Z.H., Wu, B.B., Huang, D., & Wei, W.E. (2012). Support vector machines approach to mean particle size of rock fragmentation due to bench blasting prediction. Transactions of Nonferrous Metals Society of China 22(2) 432–441.
[23]. Sayadi, A., Monjezi, M., Talebi, N., & Khandelwal, M. (2013). A comparative study on the application of various artificial neural networks to simultaneous prediction of rock fragmentation and backbreak. Journal of Rock Mechanics and Geotechnical Engineering 5(4) 318–324.
[24]. Karami, A., & Afiuni-Zadeh, S. (2013). Sizing of rock fragmentation modeling due to bench blasting using adaptive neuro fuzzy inference system (ANFIS). International Journal Mining Science and Technology 23(6) 809–813.
[25]. Bakhtavar, E., Khoshrou, H., & Badroddin, M. (2015). Using dimensional-regression analysis to predict the mean particle size of fragmentation by blasting at the Sungun copper mine. Arabian Journal for Geosciences 8(4) 2111–2120.
[26]. Ebrahimi, E., Monjezi, M., Khalesi, M.R., & Jahed Armaghani, D. (2015). Prediction and optimization of backbreak and rock fragmentation using an artificial neural network and a bee colony algorithm. Bulletin of Engineering, Geology and the Environment 75(1) 27–36.
[27]. Mandelbrot, B.B. (1983). The Fractal Geometry of Nature: Updated and Augmented. WH Freeman, New York, NY.
[28]. Cheng, Q., Agterberg, F.P., & Ballantyne, S.B. (1994). The separation of geochemical anomalies from background by fractal methods. Journal of Geochemical Exploration 51(2) 109–130.
[29]. Agterberg, F.P. (1995). Multifractal modeling of the sizes and grades of giant and supergiant deposits. International Geology Review 37(1) 1–8.
[30]. Cheng, Q., Xu, Y., & Grunsky, E. (2000) Integrated spatial and spectrum method for geochemical anomaly separation. Natural Resources, 9, 43–52.
[31]. Davis, J.C. (2002). Statistics and data analysis in geology, 3rd edition. John Wiley & Sons Inc., New York.
[32]. Li, C., Ma, T., & Shi, J. (2003). Application of a fractal method relating concentrations and distances for separation of geochemical anomalies from background. Journal of Geochemical Exploration, 77, 167–175.
[33]. Zuo, R., Xia, Q., & Zhang, D. (2013). A comparison study of the C-A and SA models with singularity analysis to identify geochemical anomalies in covered areas. Applied Geochemistry, 33, 165-172.
[34]. Afzal, P., Alghalandis, Y.F., Khakzad, A., Moarefvand, P., & Omran, N.R. (2011). Delineation of mineralization zones in porphyry Cu deposits by fractal concentration–volume modeling. Journal of Geochemical Exploration 108(3). 220–232.
[35]. Hassanpour, S., & Afzal, P. (2013). Application of concentration–number (C–N) multifractal modeling for geochemical anomaly separation in Haftcheshmeh porphyry system, NW Iran. Arabian Journal for Geosciences 6(3) 957–970.
[36]. Yasrebi, A.B., Wetherelt, A., Foster, P., Coggan, J., Afzal, P., Agterberg, F., & Kaveh Ahangaran, D. (2014). Application of a density–volume fractal model for rock characterization of the Kahang porphyry deposit. International Journal of Rock Mechanics and Mining Sciences 66 188–193
[37]. Yasrebi, A.B., Hezarkhani, A., & Afzal, P. (2017). Application of Present Value-Volume (PV-V) and NPV-Cumulative Total Ore (NPV-CTO) fractal modelling for mining strategy selection. Resources Policy 53:384–393.
[38]. Zhao, Y., Huang, J., & Wang, R. (1993). Fractal characteristics of mesofractures in compressed rock specimens. International Journal of Rock Mechanics and Mining Sciences 30(7), 877–882.
[39]. Ehlen, J., (2000). Fractal analysis of joint patterns in granite. International Journal of Rock Mechanics and Mining Sciences 37(6), 909–922.
[40]. Billi, A., & Storti, F. (2004). Fractal distribution of particle size in carbonate catallactic rocks from the core of a regional strike-slip fault zone. Tectonophysics 384, 115–128.
[41]. Hamdi, E. (2008). A fractal description of simulated 3D discontinuity networks. Rock Mechanics and Rock Engineering 41(4), 587–599.
[42]. Kruhl, J.H. (2013). Fractal-geometry techniques in the quantification of complex rock structures: a special view on scaling regimes, inhomogeneity and anisotropy. Journal of Structural Geology, 46, 2–21.
[43]. Yasrebi, A.B., Wetherelt, A., Foster, P.J., Afzal, P., Coggan, J., & Ahangaran, D.K. (2013). Application of RQD- Number and RQD-Volume to delineate rock mass characterization in Kahang Cu-Mo porphyry deposit, central Iran. Archives of Mining Sciences 58(4), 1023–1035.
[44]. Ficker, T. (2017). Fractal properties of joint roughness coefficients. International Journal of Rock Mechanics and Mining Sciences 94, 27–31.
[45]. Zhan, J., Xu, P., Chen, J., Wang, Q., Zhang, W., & Han, X. (2017). Comprehensive characterization and clustering of orientation data: A case study from the Songta dam site, China. Engineering Geology 225, 3–18.
[46]. Crum, S.V. (1990). Fractal concepts applied to bench-blast fragmentation. In: Proc. 3rd US Rock Mechachanics. Symposium Balkema, Rotterdam, 913–919.
[47]. Ghosh, A., Daemen, J.J., & Van Zyl, D. (1990). Fractal-based approach to determine the effect of discontinuities on blast fragmentation. In: The 31th US Symposium on Rock Mechanics (USRMS), American Rock Mechanics Association.
[48]. Samani, B. A., (1988). Metallogeny of the Precambrian in Iran. Precambrian research journal 39, 85–106.
[49]. Jami, M., (2005). Geology, Geochemistry and evolution of the Esfordi phosphate–iron deposit, Bafq area, central Iran. Ph.D. thesis, University of New South Wales, Sydney, Australia.
[50]. Sadeghi, B., Moarefvand, P., Afzal, P., Yasrebi, A.B., & Saein, L.D. (2012). Application of fractal models to outline mineralized zones in the zaghia iron ore deposit, central Iran. Journal of Geochemical Exploration 122, 9–19.
[51]. Afzal, P., Ghasempour, R., Mokhtari, A.R., & Haroni, H.A. (2015). Application of Concentration-Number and Concentration-Volume Fractal Models to Recognize Mineralized Zones in North Anomaly Iron Ore Deposit, Central Iran. Archives of Mining Sciences 60(3), 777–789.
[52]. Saein, L.D., Rasa, I., Omran, N.R., Moarefvand, P., Afzal, P., & Sadeghi, B. (2013). Application of number-size (N-S) fractal model to quantify of the vertical distributions of Cu and Mo in Nowchun porphyry deposit (Kerman, Se Iran). Archives of Mining Sciences 58, 89–105.
[53] Havermann, T., & Vogt, W. (1996). TUCIPS- A system for the estimation of solid particleation after production blasts. In: Franklin, J.A., Katsabanis, T. (eds.) Measurement of Blast Solid particleation, Balkema, Rotterdam 67–71.
[54]. Maerz, N.H., Palangio, T.C., & Franklin, J.A. (1996). WipFrag image based granulometry system. In Proceedings of the FRAGBLAST, 5 Workshop on Measurement of Blast Fragmentation, Montreal, 91–99.
[55]. Sanchidrián, J., Segarra, P., & López, L. (2006). A practical procedure for the measurement of fragmentation by blasting by image analysis. Rock Mechanics and Rock Engineering, 39(4), 359–382.
[56]. Siddiqui, F.I., Shah, S.A., & Behan, M.Y. (2009). Measurement of size distribution of blasted rock using digital image processing. Journal of King Abdulaziz University 20(2), 81–93
[57]. Franklin, J.A., Kemeny, J.M., & Girdner, K.K. (1996). Evolution of measuring systems: A review. Measurement of Blast Fragmentation, first edition, Routledge.
[58] Bamford, T., Esmaeili, K., & Schoellig, A.P. (2016). A real-time analysis of rock fragmentation using UAV technology. 6th International Conference on Computer Applications in the Minerals Industries, arXiv:1607.04243 [cs.RO].
[59]. Carranza, E.J.M. (2011). Analysis and mapping of geochemical anomalies using logratio-transformed stream sediment data with censored values. Journal of Geochemical Exploration 110(2), 167–185.
[60]. Yilmaz, I., & Yuksek, G. (2009). Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, and ANFIS models. International Journal of Rock Mechanics and Mining Sciences, 46(4), 803–810.
[61]. Faramarzi, F., Mansouri H, & Ebrahimi Farsangi M.A. (2013). A rock engineering systems based model to predict rock fragmentation by blasting. International Journal of Rock Mechanics and Mining Sciences, 60, 82–94.
[62]. Hasanipanah, M., Jahed Armaghani D, Monjezi, M., & Shams, S. (2016). Risk assessment and prediction of rock fragmentation produced by blasting operation: a rock engineering system. Environmental Earth Sciences, 75(9), 808. | ||
|
آمار تعداد مشاهده مقاله: 509 تعداد دریافت فایل اصل مقاله: 147 |
||