Development of a new model for blast fragmentation prediction

Transactions of the Society for Mining, Metallurgy, and Exploration , 2011, Vol. 330, No. 1, pp. 549-556

Hudaverdi, T.; Kulatilake, P.H.S.W.; Kuzu, C.

 Prediction of blast fragmentation plays a key role for overall productivity of mines. In this study, a new multivariate analysis procedure for prediction of blast fragmentation is presented. Several blasts performed in various mines and rock formations worldwide were brought together and evaluated. An extensive blast database was created. Blast design parameters, the modulus of elasticity and in-situ block size were considered to perform multivariate analysis. A complete multivariate analysis procedure was applied in three steps. First, a hierarchical cluster analysis was used to separate the blast data into different groups of similarity. Two different groups were formed based on the elasticity modulus values. In the second step, group memberships were checked by discriminant analysis. The dominant parameters of the occurrence of the groups were determined. Also, discriminant analysis was used to determine the group membership of the respective blasts. As the last step, the multivariate regression analysis was applied to develop prediction equations for estimation of the mean particle size of muckpiles. Two different prediction equations were developed based on the rock stiffness. Validation of the proposed equations on various mines was performed. Prediction capability of the proposed models was found to be strong. The models are not complex and are suitable for practical use at mines.