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Abstrakty
Backbreak is an undesirable phenomenon in blasting operations, which can bedefined as the undesirable destruction of rock behind the last row of explosive holes. To prevent and reduce its adverse effects, it is necessary to accurately predict backbreak in the blasting process. For this purpose, the data obtained from 66 blasting operations in Gol-e-Gohar iron ore mine No. 1 considering blast pattern design Parameters and geologic were collected. The Pearson correlation results showed that the parameters of the hole height, burden, spacing, specific powder, number of holes, and the uniaxial compressive strength had a significant effect on the backbreak. In this study, a multilayer perceptron artificial neural network with the 6-12-1 architecture and six multiple linear and nonlinear statistical models were used to predict the backbreakin the blasting operations. The results of this study demonstrated that the prediction rate of backbreak using the artificial neural network model with R2 = 0.798 and the rates of MAD, MSE, RMSE and, MAPE were0.79, 0.93, 0.97 and, 11.63, respectively, showed fewer minor error compared to statistical models. Based on the sensitivity analysis results, the most important parameters affecting the backbreak, including the hole height, distance between the holes in the same row, the row spacing of the holes, had the most significant effect on the backbreak, and the uniaxial compressive strength showed the lowest impact on it.
Wydawca
Czasopismo
Rocznik
Tom
Strony
107--121
Opis fizyczny
Bibliogr. 34 poz., fot., rys., tab., wykr.
Twórcy
autor
- Shahrood University of Technology, Iran
autor
- Shahrood University of Technology, Iran
autor
- Shahrood University of Technology, Iran
autor
- Technology Management and Research of Gol-e-Gohar, Iran
Bibliografia
- [1] M. Sari, E. Ghasemi, M. Ataei, Stochastic Modeling Approach for the Evaluation of Backbreak due to Blasting Operations in Open Pit Mines. Rock Mechanics and Rock Engineering 47, 771-783 (2014).
- [2] C.J. Konya, E.J. Walter, Surface blast design. Prentice Hall, Englewood Cliffs (1990).
- [3] D.P. Blair, L.W. Armstrong, The influence of burden on blast vibration. International Journal for Blasting and Fragmentation 5, 108-129 ( 2002).
- [4] W.C. Gate, R.M. Florez, L. Ortiz, Analysis of rockfall and blasting backbreak problems. In: Paper ARMA/USRMS, Proceedings of the American Rock Mechanics Conference 5, 671-80 (2005).
- [5] R. Gustafsson, Swedish Blasting Technique. Published by SPI, Gothenburg, Sweden 61-62 (1973).
- [6] U. Langefors, B. Kihlström, The modern technique of rock blasting. 3rd ed,Stockholm: AWE/GEBERS; 1978.
- [7] W. Hustrulid, Blasting Principles for Open Pit Mining. General Design Concepts, A ABalkema, Rotterdam, Netherlands (1999).
- [8] T.N. Hagan, B. Bulow, Blast designs to protect pit walls. In: Hustrulid WA, McCarter MK, Van Zyl DJA (eds.) Slope stability in surface mining. Society of Mining, Metallurgy and Exploration, Denver, 125-130 (2000).
- [9] W.A. Hustrulid, W.B. Lu, Some general design concepts regarding the control of blast-induced damage during rock slope excavation. In: Proceedings of the 7th International Symposium on Rock Fragmentation by Blasting, Beijing, China, August, 595-604 (2002).
- [10] JC.J. Hanwar, J.L. Jethwa, The use of air-decks in production blasting in an open pit Coal mine. Geotech. Geol. Eng. 18, 269-287 (2000).
- [11] A. Aghajani Bazzazi, H. Mansouri, M.A. Ebrahimi Farsangi, A. Atashpanjeh, Application of controlled blasting (pre-splitting) using large diameter holes in Sarcheshmeh Copper Mine. In: Proceedings of the 8th International Symposium on Rock Fragmentation by Blasting, 388-392 (2006).
- [12] P.K. Singh, M.P. Roy, A. Joshi, V.P. Joshi, Controlled blasting (pre-splitting) at an open-pit mine in India. In: Proceedings of the 9th International Symposium on Rock Fragmentation by Blasting, 481-489 (2009).
- [13] S. Bhandari, Engineering rock blasting operations. Balkema, Rotterdam (1997).
- [14] J.M. Wilson, N.T. Moxon, The development of low energy ammonium nitrate based explosives. Proceedings of the Australasian Institute of Mining and Metallurgy, Melbourne, Australia 27-32 (1988).
- [15] I. Enayatollahi, A. Aghajani-Bazzazi, Evaluation of salt-ANFO mixture in backbreak reduction by data envelopment analysis. In: Proceedings of the 9th International Symposium on Rock Fragmentation by Blasting 127-133 (2009).
- [16] S.R. Iverson, W.A. Hustrulid, J.C. Johnson, D. Tesarik, Y. Akbarzadeh, The extent of blast damage from a fully coupled explosive charge, Proceedings of the ninth international symposium on rock fragmentation by blasting. Granada, Spain: Taylor & Francis, 459-68 (2009).
- [17] S. Bhandari, R. Badal, Relationship of joint orientation with hole spacing parameter in multihole blasting. Proceedings of the 3rd International Symposium on Rock Fragmentation by Blasting, Brisbane, Australia, August 225-231 (1990).
- [18] Z. Jia, G. Chen, S. Huang, Computer simulation of open pit bench blasting in jointed rock mass. Rock Mechanics and Mining Sciences 35 (4-5), 476 (1998). DOI: https://doi.org/10.1016/S0148-9062(98)00137-5.
- [19] M. Monjezi, A. Gholinejad, Application of TOPSIS method for selecting the most appropriate blast design. Arabian Journal of Geosciences 5, 95-101 (2012). DOI: https://doi.org/10.1007/s12517-010-0133-2.
- [20] F. Maulenkamp, M.A. Grima, Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness. Rock Mechanics and Mining Sciences 36, 29-39 (1999). DOI: https://doi. rg/10.1016/S0148-9062(98)00173-9.
- [21] M. Monjezi, H. Dehghani, Evaluation of effect of blasting pattern parameters on back break using neural networks. Rock Mechanics & Mining Sciences 45, 1446-1453 (2008). DOI: https://doi.org/10.1016/j.ijrmms.2008.02.007.
- [22] H. Dehghani, M. Ataeepour, Development of a model to predict peak particle velocity in a blasting operation. Rock Mechanics and Mining Sciences 48, 51-58 (2012). DOI: https://doi.org/10.1016/j.ijrmms.2010.08.005.
- [23] L. Amayreh, M.P. Saka, Failure load prediction of castellated beams using artificial neural networks. Asian Journalof Civil Engineering 6 (1-2), 35-54 (2005). Corpus ID: 14394600.
- [24] Z. Wang, Z. Wang, Y. Liu, P.J. Griffin, A combined ANN and expert system tool for transformer fault diagnosis. Power Engineering Society Winter Meeting 2, 1261-1269 (2000). DOI: https://doi.org/10.1109/PESW.1999.747476.
- [25] I. Uckan, T. Yılmaz, E. Hürdoğan, O. Büyükalaca, Development of an Artificial Neural Network Model for the Prediction of the Performance of a Silica-gel Desiccant Wheel. Green Energy 12, 1159-1168 (2015). DOI: https://doi.org/10.1080/15435075.2014.895733.
- [26] A. Alvarez Grima, P.N.W. Verhoef, Forecasting rock trencher performance using fuzzy logic. Rock Mechanics and Mining Sciences and Geomechanics 36 (4), 413-432 (1999). DOI: https://doi.org/10.1016/S0148-9062(99)00025-X.
- [27] J. Finol, Y.K. Guo, D.J. Xudong, A rule based fuzzy model for the prediction of petrophysical rock parameters. Petroleum Science and Engineering 29, 97-113 (2001). DOI: https://doi.org/10.1016/S0920-4105(00)00096-6.
- [28] C. Gokceoglu, K. Zorlu, A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock. Engineering Applications of Artificial Intelligence 17, 61-72 (2004). DOI: https://doi.org/10.1016/j.engappai.2003.11.006.
- [29] M. Monjezi, A. Bahrami, A.Y. Varjani, Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks. Rock Mechanics and Mining Sciences 47 (3), 476-480 (2010). DOI: https://doi.org/10.1016/j.ijrmms.2009.09.008.
- [30] M. Monjezi, M. Ghafurikalajahi, A. Bahrami, Prediction of blast-induced ground vibration using artificial neural networks, Tunnelling and Underground Space Technology 26 (1), 46-50 (2010). DOI: https://doi.org/10.1016/j.tust.2010.05.002.
- [31] M. Monjezi, M. Rezaei, A. Yazdian, Prediction of backbreak in open-pit blasting using fuzzy set theory. Expert Systems with Applications 37, 2637-2643 (2010). DOI: https://doi.org/10.1016/j.eswa.2009.08.014.
- [32] MA. Razi, K. Athappilly, A comparative predictive analysis of neural networks (NNs), nonlinear regression and classification and regression tree (CART) models. Expert Systems with Applications 29, 65-74 (2005). DOI: https://doi.org/10.1016/j.eswa.2005.01.006.
- [33] Y. Hun Jong, C. In Lee, Influence of geological conditions on the powder factor for tunnel blasting. Rock Mechanics and Mining Sciences 41 (1), 533-538 (2004). DOI: https://doi.org/10.1016/J.IJRMMS.2004.03.095.
- [34] M. Khandelwal, T.N. Singh, Evaluation of blast-induced ground vibration predictors. Soil Dynamics and Earthquake Engineering 27 (2), 116-125 (2007). DOI: https://doi.org/10.1016/J.SOILDYN.2006.06.00.
Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b79e1208-306c-414a-8160-1901e26c1353