Application of artificial neural networks for predicting the cuttability of rocks by drag tools

Many models have previously been developed for predicting specific cutting energy (SE), being the measure of rock cuttability, from intact rock properties employing conventional multiple linear or nonlinear regression techniques. Artificial neural networks (ANN) also have a great potential in building such models. This paper is concerned with the application of ANN for the prediction of cuttability of rocks from their intact properties. For that purpose, data obtained from three different projects were subjected to statistical analyses using MATLAB. Principal components analysis together with the scatterplots of SE against intact rock properties were employed to select the predictors for SE models. Results of the principal components analysis have shown that the most of the variance in the data set can be explained by three principal components. Principal component with the highest variance is weighted mainly on the uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), static modulus of elasticity (Elasticity), and cone indenter hardness (CI), which were regarded as the independent variables driving the data set. Three predictive models for SE were developed employing above independent variables by multiple nonlinear regression with forward stepwise method and ANN, respectively. Neural networks were developed for two different numbers of hidden neurons in the hidden layer. Goodness of the fit measures revealed that ANN models fitted the data as accurately as multiple nonlinear regression model, indicating the usefulness of artificial neural networks in predicting rock cuttability.