TY  - JOUR
PY  - 2016//
TI  - Prediction of blast boulders in open pit mines via multiple regression and artificial neural networks
JO  - International journal of mining science and technology
A1  - Ghiasi, Majid
A1  - Askarnejad, Nematollah
A1  - Dindarloo, Saeid R.
A1  - Shamsoddini, Hamed
SP  - 183
EP  - 186
VL  - 26
IS  - 2
N2  - 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. <br><br>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.<p />  <p>Language: en</p>
LA  - en
SN  - 2095-2686
UR  - http://dx.doi.org/10.1016/j.ijmst.2015.12.001
ID  - ref1
ER  -