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Prediction of blast boulders in open pit mines via multiple regression and artificial neural networks 被引量:3
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作者 ghiasi majid Askarnejad Nematollah +1 位作者 Dindarloo Saeid R. Shamsoddini Hamed 《International Journal of Mining Science and Technology》 SCIE EI CSCD 2016年第2期183-184,共2页
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 boul... 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. 展开更多
关键词 露天矿爆破 神经网络预测 人工神经网络 多元回归法 石块 单轴抗压强度 节理间距 关键参数
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