Abstract

To determine rock mechanical properties like uniaxial compressive strength and shear strength accurately, it is required to put considerable time to find and collect suitable samples for laboratory testing. To improve the time and cost efficiency, many empirical relationships have been proposed in literature. The purpose of this study is to develop a model to correlate uniaxial compressive strength and shear strength data of intact rocks. In this study, two mathematical methods, adaptive neuro-fuzzy inference systems (ANFIS) and regression analysis, were used to correlate the uniaxial compressive and shear strength. A new approach based on artificial intelligence techniques is considered to develop and train UCS-τ data. A total of 40 sets of data were used to correlate UCS and τ data of limestone rocks. The resulted regression equation shows that the relationship between uniaxial compressive and shear strength has an acceptable determination coefficients of R2. Results of this research study has also indicated that, because of their acceptable accuracy in development of an efficient correlation between UCS and τ data, adaptive neuro-fuzzy inference systems are appropriate tools to correlate UCS-τ data, in addition to the regression model proposed in this paper.

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