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A comparative study on the application of various artificial neural networks to simultaneous prediction of rock fragmentation and backbreak

查看全文 作　　者：[1]A.Sayadi;[2]M.Monjezi;[1]N.Talebi;Manoj [3]Khandelwal 高影响力作者 机构地区：[1]Islamic Azad University,Tehran South Branch,Tehran,Iran;[2]Faculty of Engineering,Tarbiat Modares University;[3]Maharana Pratap University of Agriculture and Technology高影响力机构 出　　处：《Journal of Rock Mechanics and Geotechnical Engineering》索引2013年第5卷第4期,共7页高影响力期刊 摘　　要：In blasting operation,the aim is to achieve proper fragmentation and to avoid undesirable events such as backbreak.Therefore,predicting rock fragmentation and backbreak is very important to arrive at a technically and economically successful outcome.Since many parameters affect the blasting results in a complicated mechanism,employment of robust methods such as artificial neural network may be very useful.In this regard,this paper attends to simultaneous prediction of rock fragmentation and backbreak in the blasting operation of Tehran Cement Company limestone mines in Iran.Back propagation neural network(BPNN) and radial basis function neural network(RBFNN) are adopted for the simulation.Also,regression analysis is performed between independent and dependent variables.For the BPNN modeling,a network with architecture 6-10-2 is found to be optimum whereas for the RBFNN,architecture 636-2 with spread factor of 0.79 provides maximum prediction aptitude.Performance comparison of the developed models is fulfilled using value account for(VAF),root mean square error(RMSE),determination coefficient(R2) and maximum relative error(MRE).As such,it is observed that the BPNN model is the most preferable model providing maximum accuracy and minimum error.Also,sensitivity analysis shows that inputs burden and stemming are the most effective parameters on the outputs fragmentation and backbreak,respectively.On the other hand,for both of the outputs,specific charge is the least effective parameter. 关 键 词：人工神经网络 预测 BP神经网络模型 岩石 径向基函数神经网络 RBF神经网络 径向基神经网络 应用
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