Prediction of blast boulders in open pit mines via multiple regression and artificial neural networks |
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| Affiliation: | 1. Kusha Ma’dan Consulting Engineers Co., Tehran 11359, Iran;2. Missouri University of Science and Technology, Rolla 65409, USA;3. Bahonar University, Kerman 76169, Iran;1. State Key Laboratory Cultivation Base for Gas Geology and Gas Control, Henan Polytechnic University, Jiaozuo 454003, China;2. State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou 221116, China;1. State Key Laboratory of Deep Coal Mining & Environmental Protection, Huainan 232001, China;2. School of Energy and Safety, Anhui University of Science & Technology, Huainan 232001, China;3. Key Laboratory of Safety and High-efficiency Coal Mining, Ministry of Education, Anhui University of Science & Technology, Huainan 232001, China;1. School of Science, China University of Mining & Technology, Xuzhou 221116, China;2. State Key Laboratory for Geomechanics & Deep Underground Engineering, China University of Mining & Technology, Xuzhou 221116, China;1. School of Mines, China University of Mining & Technology, Xuzhou 221116, China;2. Key Laboratory of Deep Coal Resource Mining, Ministry of Education of China, Xuzhou 221116, China;1. Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, India;2. Central Institute of Mining and Fuel Research, Regional Centre, CBRI Campus, Roorkee, India;1. Mining Engineering Department, King Abdulaziz University, Jeddah, Saudi Arabia;2. Department of Earth Sciences, Indian Institute of Technology-Bombay, Mumbai 400076, India |
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| Abstract: | The most important objective of blasting in open pit mines is rock fragmentation.Prediction of produced boulders(oversized crushed rocks) is a key parameter in designing blast patterns.In this study,the amount of boulder produced in blasting operations of Golegohar iron ore open pit mine,Iran was predicted via multiple regression method and artificial neural networks.Results of 33 blasts in the mine were collected for modeling.Input variables were:joints spacing,density and uniaxial compressive strength of the intact rock,burden,spacing,stemming,bench height to burden ratio,and specific charge.The dependent variable was ratio of boulder volume to pattern volume.Both techniques were successful in predicting the ratio.In this study,the multiple regression method was superior with coefficient of determination and root mean squared error values of 0.89 and 0.19,respectively. |
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| Keywords: | Blast boulder Artificial neural networks Multiple regression Golegohar iron ore mine |
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