Thisstudyaimstodevelopseveralpredictionmodelsofuniaxialcompressivestrength(UCS)and elastic modulus(E)of different migmatite rocks from four areas of the Sanandaj-Sirjanzonein Iran. Inaddition 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 V P; (5) BPI, BTS, IS(50), and V P. 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.
