Prediction of blast boulders in open pit mines via multiple regression and artificial neural networks  

作　　者：Ghiasi Majid Askarnejad Nematollah Dindarloo Saeid R. Shamsoddini Hamed 

机构地区：[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

出　　处：《International Journal of Mining Science and Technology》2016年第2期183-184,共2页矿业科学技术学报（英文版）

摘　　要：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.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.

关 键 词：Blast boulder Artificial neural networks Multiple regression Golegohar iron ore mine 

分 类 号：TD854.2[矿业工程—金属矿开采] TQ336.1[矿业工程—矿山开采]