Optimizing overbreak prediction based on geological parameters comparing multiple regression analysis and artificial neural network |
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| Affiliation: | 1. Department of Mining Engineering and Metallurgical Engineering, Western Australian School of Mines, Curtin University, Perth 6102, Australia;2. Division of Intelligent Interaction Technologies, Faculty of Engineering, Information and Systems, University of Tsukuba, Tsukuba 3058555, Japan;1. Department of Geology, University of Lucknow, Lucknow, Uttar Pradesh 226007, India;2. Department of Earth Sciences, Indian Institute of Technology Bombay, Mumbai, Maharashtra 400076, India;1. Institute of Geotechnical Engineering, University of Stuttgart, Stuttgart, Germany;2. Director of Earth Mechanics Institute, Department of Mining Engineering, Colorado School of Mines, USA;3. INGPHI, Ingénieurs en ouvrages d''art, Lausanne, Switzerland;1. Division of Economics and Business, Colorado School of Mines, Golden, CO 80401, United States;2. Department of Mechanical Engineering, Colorado School of Mines, Golden, CO 80401, United States;1. Mining Engineering Department, Faculty of Engineering, Tarbiat Modares University, Tehran, Iran;2. School of Mechanical and Mining Engineering, The University of Queensland, Brisbane, Australia |
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| Abstract: | Underground mining becomes more efficient due to the technological advancements of drilling and blasting methods and the developing of highly productive mining methods that facilitate easier access to ore. In the perspective of maximizing productivity in underground mining by drilling and blasting methods, overbreak control is an essential component. The causing factors of overbreak can simply divided as blasting and geological parameters and all of the factors are nonlinearly correlated. In this paper, the blasting design of the tunnel was fixed as the standard blasting pattern and the research focus on effects of geological parameters to the overbreak phenomenon. 49 sets of rock mass rating (RMR) and overbreak data were applied to linear and nonlinear multiple regression analysis (LMRA and NMRA) and artificial neural network (ANN) to predict overbreak as input and output parameters, respectively. The performance of LMRA, NMRA, and optimized ANN models was evaluated by comparing coefficient correlations (R2) and their values are 0.694, 0.704 and 0.945, respectively, which means that the relatively high level of accuracy of the optimized ANN in comparison with LMRA and NMRA. The developed optimum overbreak predicting ANN model is suitable for establishing an overbreak warning and preventing system and it will utilize as a foundation reference for a practical drift blasting reconciliation at mines for operation improvements. |
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| Keywords: | Blasting Overbreak Underground mine Artificial neural network Multiple regression analysis |
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