Document Type : Original Research Paper

Authors

School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran

Abstract

The present work aims at implementing Response Surface Methodology (RSM) in order to generate a statistical model for Minimum Required Caving Span (MRCS) and estimate both the individual and mutual effects of the rock mass parameters on rock mass cavability. The adequate required data is obtained from the result of numerical modeling. In this work, various arrays of numerical simulations (480 models) are carried out using the UDEC software in order to study the rock mass cavability thoroughly. The effect of each individual parameter and their mutual effect on MRCS are investigated by means of ANOVA. ANOVA indicates that all the chosen parameters (depth, dip of the joint, number of joints, angle of friction of the joint surface, and joint spacing) highly affect MRCS. In other words, the results of ANOVA are in high agreement with the results of the conventional sensitivity analysis. Moreover, a combination of joint spacing and joint inclination has the highest mutual effect on MRCS, and a combination of undercut depth and joint spacing has the lowest effect on MRCS.

Keywords

[1]. Brady, B.H., and Brown, E.T. (2006). Rock mechanics: for underground mining. Springer science & business media.
[2]. Mawdesley, C.A. (2002). Predicting rock mass cavability in block caving mines.
[3]. Brannon, C.A., Carlson, G.K., and Casten, T.P. (2011). Block Caving & Cave Mining. SME Mining Engineering Handbook, 1437-1450.
[4]. Rice, G. S. (1934). Ground movement from mining in Brier Hill mine, Norway, Michigan. Mining and Metallurgy. 15 (325): 12-14.
[5]. Panek, L.A. (1984). Subsidence in undercut-cave operations, subsidence resulting from limited extraction of two neighboring-cave operations. Geomechanical applications in hard rock mining, 225-240.
[6]. Beck, D., Sharrock, G., and Capes, G. (2011). A coupled DFE-Newtonian Cellular Automata scheme for simulation of cave initiation, propagation and induced seismicity. 45th US Rock Mechanics/Geomechanics Symposium. American Rock Mechanics Association.
[7]. Carlson, G., and Golden, R. (2008). Initiation, Growth, Monitoring and Management of the 7210 Cave at Henderson Mine---A Case Study. In 5th International Conference and Exhibition on Mass Mining (Vol. 9, pp. 97-106).
[8]. Ross, I., and Van As, A. (2005). Northparkes Mines-design, sudden failure, air-blast and hazard management at the E26 block cave. In 9th AusIMM Underground Operators Conference 2005 (Vol. 2005, No. 1, pp. 7-18). AUSTRALASIAN INST MINING & METALLURGY.
[9Someehneshin, J., Oraee-Mirzamani, B., and Oraee, K. (2015). Analytical model determining the optimal block size in the block caving mining method. Indian Geotechnical Journal. 45 (2): 156-168.
[10]. Rafiee, R., Ataei, M., and KhalooKakaie, R. (2015). A new cavability index in block caving mines using fuzzy rock engineering system. International Journal of Rock Mechanics and Mining Sciences, 77, 68-76.
[11]. Rafiee, R., Ataei, M., KhaloKakaie, R., Jalali, S. E., and Sereshki, F. (2016). A fuzzy rock engineering system to assess rock mass cavability in block caving mines. Neural Computing and Applications. 27 (7): 2083-2094.
[12]. Mathews, K.E, Hoek, E., Wyllie, D.C, and Stewart, S. (1981). Prediction of stable excavation spans for mining at depths below 1000 m in hard rock.
[13]. Tobie, R.L. and Julin, D. E. (1984). Block Caving: General Description in Underground Mining Methods Handbook, W. A. Hustrulid, ed., Soc. Mng. Enngr, New York, pp. 967–972.
[14]. Laubscher, D. (2000). Cave Mining Handbook.
[15]. Milne, D., Hadjigeorgiou, J., and Pakalnis, R. (1998). Rock mass characterization for underground hard rock mines. Tunnelling and underground space technology. 13 (4): 383-391.
[16]. Palma, R., and Agarwal, R. (1973). A study of the cavability of primary ore at the El Teniente Mine. Technical Report from Colombia University, New York.
[17]. Lorig, L. (2000). The Role of Numerical Modelling in Assessing Cavability. Itasca Consulting Group, Inc., Report to the International Caving Study. ICG00-099-3-16, October 22–25 (niepublikowane).
[18]. Brown, E.T. (2002). Block caving geomechanics.
[19]. Brown, E.T. (2003). Block Caving Geo-mechanics. The International Caving Study I. JKMRC Monograph Series in Mining and Mineral Processing 3. University of Queensland.
[20]. Pierce, M., Cundall, P., Potyondy, D., and Ivars, D.M. (2007, May). A synthetic rock mass model for jointed rock. In 1st Canada-US Rock Mechanics Symposium. OnePetro.
[21]. Ivars, D.M., Pierce, M.E., Darcel, C., Reyes-Montes, J., Potyondy, D.O., Young, R.P., and Cundall, P.A. (2011). The synthetic rock mass approach for jointed rock mass modelling. International Journal of Rock Mechanics and Mining Sciences. 48 (2): 219-244.
[22]. Sainsbury, B. (2012). A model for cave propagation and subsidence assessment in jointed rock masses. University of South Wales.
[23]. Woo, K.S., Eberhardt, E., Rabus, B., Stead, D., and Vyazmensky, A. (2012). Integration of field characterisation, mine production and InSAR monitoring data to constrain and calibrate 3-D numerical modelling of block caving-induced subsidence. International Journal of Rock Mechanics and Mining Sciences, 53, 166-178.
[24]. Rafiee, R., Ataei, M., KhalooKakaie, R., Jalali, S.E., Sereshki, F., and Noroozi, M. (2018). Numerical modeling of influence parameters in cavabililty of rock mass in block caving mines. International Journal of Rock Mechanics and Mining Sciences, 105, 22-27.
[25]. Xia, Z., Tan, Z., Pei, Q., and Wang, J. (2019). Ground pressure damage evolution mechanism of extraction level excavations induced by poor undercutting in block caving method. Geotechnical and Geological Engineering. 37 (5): 4461-4472.
[26]. Xia, Z., Tan, Z., and Miao, Y. (2020). Damage evolution mechanism of extraction structure during mining gently dipped orebody by block caving method. Geotechnical and geological engineering. 38 (4): 3891-3902.
[27]. Wang, J., Wei, W., Zhang, J., Mishra, B., and Li, A. (2020). Numerical investigation on the caving mechanism with different standard deviations of top coal block size in LTCC. International Journal of Mining Science and Technology. 30 (5): 583-591.
[28]. Mohammadi, S., Ataei, M., and Kakaie, R. (2018). Assessment of the importance of parameters affecting roof strata cavability in mechanized longwall mining. Geotechnical and Geological Engineering. 36 (4): 2667-2682.
[29]. McNearny, R.L., and Abel Jr, J.F. (1993, April). Large-scale two-dimensional block caving model tests. In International journal of rock mechanics and mining sciences & geomechanics abstracts (Vol. 30, No. 2, pp. 93-109). Pergamon.
[30]. Carmichael. P. and Hebblewhite. B. (2012). An investigation into semi-intact rock mass representation for physical modelling block caving mechanics’ zone. Mining education Australian Research projects review.
[31]. Cumming-Potvin, D., Wesseloo, J., Jacobsz, S. W., and Kearsley, E. (2016). Fracture banding in caving mines. Journal of the Southern African Institute of Mining and Metallurgy. 116 (8): 753-761.
[32]. Obert, L., E. (1973). Design and Stability of Excavation, Sec. 7 in SME Mining Engineering Handbook, A. B. Cummins and I. A. Given, Ed., Soc. Mng. Enngr, New York, p. 49.
[33]. Darling, P. (Ed.). (2011). SME mining engineering handbook (Vol. 1). SME.
[34]. Jakubec, J. and E. (2007). Use of the Mining Rock Mass Rating (MRMR) Classification, Proceedings of the International Workshop on Rock Mass Classification in Underground Mining, pp. 73–78.
[35]. Suorineni, F.T. (2010). The stability graph after three decades in use: experiences and the way forward. International journal of mining, Reclamation and Environment. 24 (4): 307-339.
[36]. Vakili, A., and Hebblewhite, B.K. (2010). A new cavability assessment criterion for longwall top coal caving. International Journal of Rock Mechanics and Mining Sciences. 47 (8): 1317-1329.
[37]. Brummer, R.K. (2005). The South African Institute of Mining and Metallurgy International Symposium on Stability of Rock Slopes in Open Pit Mining and Civil Engineering, pp. 411–420.
[38]. Gilbride, L.J., Free, K.S., and Kehrman, R. (2005, June). Modeling Block Cave Subsidence at the Molycorp, Inc., Questa Mine? A Case Study. In Alaska Rocks 2005, The 40th US Symposium on Rock Mechanics (USRMS). OnePetro.
[39]. Vyazmensky, A., Elmo, D., and Stead, D. (2010). Role of rock mass fabric and faulting in the development of block caving induced surface subsidence. Rock mechanics and rock engineering. 43 (5): 533-556.
 [40]. Montgomery, D.C. (2001). Design and analysis of experiments. John Wiley & Sons. Inc., New York, 1997, 200-1.
[41]. Kirmizakis, P., Tsamoutsoglou, C., Kayan, B., and Kalderis, D. (2014). Subcritical water treatment of landfill leachate: Application of response surface methodology. Journal of environmental management. 146: 9-15.
[42]. Sodeifian, G.H., Azizi, J., and Ghoreishi, S.M. (2014). Response surface optimization of Smyrnium cordifolium Boiss (SCB) oil extraction via supercritical carbon dioxide. The Journal of Supercritical Fluids. 95: 1-7.
[43]. Yuan, Z., Yang, J., Zhang, Y., and Zhang, X. (2015). The optimization of air-breathing micro direct methanol fuel cell using response surface method. Energy. 80: 340-349.
[44]. Asadizadeh, M., Moosavi, M., Hossaini, M.F., and Masoumi, H. (2018). Shear strength and cracking process of non-persistent jointed rocks: an extensive experimental investigation. Rock Mechanics and Rock Engineering. 51 (2): 415-428.
[45]. Heidarzadeh, S., Saeidi, A., and Rouleau, A. (2018). Assessing the effect of open stope geometry on rock mass brittle damage using a response surface methodology. International Journal of Rock Mechanics and Mining Sciences, 106, 60-73.
[46]. Rastbood, A., Gholipour, Y., and Majdi, A. (2017). Finite element-based response surface methodology to optimize segmental tunnel lining. Engineering, Technology & Applied Science Research. 7 (2): 1504-1514.
[47]. Asadizadeh, M., Moosavi, M., and Hossaini, M.F. (2018). Investigation of the mechanical behaviour of non-persistent jointed blocks under uniaxial compression. Geomech. Eng. 14 (1): 29-42.
[48]. Miller, D.M. (1984). Reducing transformation bias in curve fitting. The American Statistician. 38 (2): 124-126.