Prediction of Uniaxial Compressive Strength of Different Quarried Rocks Using Metaheuristic Algorithm

The direct measurement of uniaxial compressive strength (UCS) as one of the main important rock engineering parameters is destructive, cumbersome, difficult and costly. Therefore, the prediction of this parameter using simpler, cheaper indirect methods is of interest. In this paper, the UCS was predicted using a developed hybrid intelligent model including generalized feedforward neural network (GFFN) incorporated with imperialist competitive algorithm (ICA). To find the optimum model, 197 sets including rock class, density, porosity, P-wave velocity, point load index and water absorption from almost all over quarries of Iran were compiled. The efficiency and performance of GFFN and hybrid ICA-GFFN models subjected to different error criteria and established confusion matrixes were compared to multilayer perceptron (MLP) and radial basis function (RBF) neural network models as well as conducted multivariate regression. The hybrid ICA-GFFN with 11.37%, 14.27% and 22.74% improvement in correct classification rate over than GFFN, RBF and MLP demonstrated superior predictability level. The results indicated that the developed ICA-GFFN model as a feasible and accurate enough tool can effectively be applied for UCS prediction purposes. Using the sensitivity analyses, the P-wave velocity and rock class were identified as the most and least influences factors on predicted UCS.