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

Ghiasi; Majid; Askarnejad; Nematollah; Dindarloo; Saeid; R.; Shamsoddini; Hamed

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

机译：多元回归和人工神经网络预测露天矿中的巨石

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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.

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《矿业科学技术学报：英文版》 |2016年第002期|P.183-184|共2页
作者
Ghiasi; Majid; Askarnejad; Nematollah; Dindarloo; Saeid; R.; Shamsoddini; Hamed;
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作者单位

[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;

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收录信息中国科学引文数据库(CSCD);
原文格式 PDF
正文语种 CHI
中图分类 TD854.2;
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;

机译：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;

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

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