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

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• 作者：Majid Ghiasi^a ; Nematollah Askarnejad^a ; Saeid R. Dindarloo^b ; ^{srd5zb@mst.edu" class="auth_mail" title="E-mail the corresponding author} ; Hamed Shamsoddini^c
• 关键词：Blast boulder ; Artificial neural networks ; Multiple regression ; Golegohar iron ore mine
• 刊名：International Journal of Mining Science and Technology
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：26
• 期：2
• 页码：183-186
• 全文大小：990 K

文摘

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.