Application of soft computing methods in predicting uniaxial compressive strength of the volcanic rocks with different weathering degree

Abstract

Uniaxial compressive strength (UCS) of rock material is very important parameter for rock engineering applications such as rock mass classification, numerical modelling bearing capacity, mechanical excavation, slope stability and supporting with respect to the engineering behaviors' of rock. UCS is obtained directly or can be predicted by different methods including using existing tables and diagrams, regression, Bayesian approach and soft computing methods. The main purpose of this study is to examine the applicability and capability of the Extreme Learning Machine (ELM), Minimax Probability Machine Regression (MPMR) for prediction of UCS of the volcanic rocks and to compare its performance with Least Square Support Vector Machine (LS-SVM). The samples tested were taken from the volcanic rock masses exposed at the eastern Pontides (NE Turkey). In the soft computing model to estimate UCS of the samples investigated, porosity and slake durability index were used as input parameters. In this study, the root mean square error (RMSE), variance account factor (VAF), maximum determination coefficient value (R-2), adjusted determination coefficient (Adj. R-2) and performance index (PI), regression error characteristic (REC) curve and Taylor diagram were used to determine the accuracy of the ELM, MPMR and LS-SVM models developed.