Prediction of uniaxial compressive strength and elastic modulus of migmatites using various modeling techniques

This study aims to develop several prediction models of uniaxial compressive strength (UCS) and elastic modulus (E) of different migmatite rocks from four areas of the Sanandaj-Sirjan zone in Iran. In addition to UCS and E, porosity, cylindrical punch Index (CPI), block punch index (BPI), Brazilian tensile strength (BTS), point load index (IS(50)), and P wave velocity (VP) were measured for migmatites. Various methods, like multiple regression (MR) analysis, artificial neural network (ANN), and adaptive neural fuzzy inference system (ANFIS), were used to predict UCS and E during the modeling process. In this study, a total of 120 inputs and outputs were used. According to the analyses performed in this study and the input parameters, five different models have been used to estimate UCS and E: (1) CPI, BPI, BTS, and IS(50); (2) CPI, BPI, BTS, and VP; (3) CPI, BPI, IS(50), and VP; (4) CPI, BTS, IS(50), and VP; (5) BPI, BTS, IS(50), and VP. Performance evaluation shows that ANN is a better prediction method compared to the others, and models 2, 4, and 5 are the best models for prediction. The developed models in this paper can have high prediction efficiency if they are used for similar types of rocks.