Predicting the cuttability of rocks using artificial neural networks and regression trees

Tiryaki B.

20th International Mining Congress and Exhibition of Turkey (IMCET 2007), Ankara, Turkey, 6 - 08 June 2007, pp.171-181 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • City: Ankara
  • Country: Turkey
  • Page Numbers: pp.171-181


This paper is concerned with the applications of artificial neural networks (ANN) and regression trees along with the multivariate statistical tools for predicting specific cutting energy (SE) of rocks from their intact properties. For that purpose, data obtained from three rock cutting projects have been subjected to statistical analyses using MATLAB software. Principal components and factor analyses have shown that uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), static modulus of elasticity (Elasticity), and cone indenter hardness (CI) seemed to be the most influential independent variables in the data set. Hierarchical cluster tree analysis has divided the variables into three different natural clusters.