Prediction of blast fragmentation using multivariate analysis procedures

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)


An extensive multivariate analysis procedure for prediction of blast fragmentation distribution is presented. Several blasts performed in various mines and rock formations in the world are brought together and evaluated. Blast design parameters, the modulus of elasticity, in situ block size are considered to perform multivariate analysis. The hierarchical cluster analysis is used to separate the blasts data into different groups of similarity. Group memberships were checked by the discriminant analysis. The multivariate regression analysis was applied to develop prediction equations for the 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 is presented and the capability of the prediction equations was compared with one of the most applied fragmentation distribution models appearing in the blasting literature. Prediction capability of the proposed models was found to be strong. Diversity of the blasts data used is one of the most important aspects of the developed models. The models are not complex and suitable for practical use at mines.b

Original languageEnglish
Pages (from-to)1318-1333
Number of pages16
JournalInternational Journal for Numerical and Analytical Methods in Geomechanics
Issue number12
Publication statusPublished - 25 Aug 2011


  • Blasting
  • Cluster analysis
  • Discriminant analysis
  • Fragmentation
  • Rock mass


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