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• W2019919549 abstract "Abstract The main goal of this study is to develop some prediction models for the UCS of six different granitic rocks selected from Turkey. During the modeling stage of the study, various approaches such as multiple regression, Artificial Neural Network (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS) are applied to estimate UCS. Tensile strength (σt), block punch index (BPI), point load index (Is(50)) and P-wave velocity (Vp) are considered as the input parameters for the models. In the study, total 75 cases including all inputs and output are used. In accordance with the analyses employed in the study, and considering the inputs, three different models are constructed as tensile strength and P-wave velocity (Model 1), BPI and P-wave velocity (Model 2), Is(50) and P-wave velocity (Model 3) to estimate UCS. Performance assessments show that ANFIS is the better predictive tool than the other methods employed, and Model 1 is the better model for the prediction of UCS. The results show that the models developed can be used as preliminary stages of rock engineering assessments because the models developed herein have high prediction performances. It is evident that such prediction studies provides not only some practical tools but also understanding of the controlling index parameters of UCS of rocks." @default.
• W2019919549 created "2016-06-24" @default.
• W2019919549 creator A5009051563 @default.
• W2019919549 creator A5077507481 @default.
• W2019919549 creator A5087050752 @default.
• W2019919549 date "2013-09-01" @default.
• W2019919549 modified "2023-10-18" @default.
• W2019919549 title "Prediction of uniaxial compressive strength of granitic rocks by various nonlinear tools and comparison of their performances" @default.
• W2019919549 cites W1560425706 @default.
• W2019919549 cites W1966064761 @default.
• W2019919549 cites W1968143675 @default.
• W2019919549 cites W1974025667 @default.
• W2019919549 cites W1976842360 @default.
• W2019919549 cites W1977168082 @default.
• W2019919549 cites W1982341542 @default.
• W2019919549 cites W1983687382 @default.
• W2019919549 cites W1985649386 @default.
• W2019919549 cites W1986507289 @default.
• W2019919549 cites W1987048772 @default.
• W2019919549 cites W1992474881 @default.
• W2019919549 cites W1993695189 @default.
• W2019919549 cites W1994665444 @default.
• W2019919549 cites W1997388854 @default.
• W2019919549 cites W1997493987 @default.
• W2019919549 cites W1999004211 @default.
• W2019919549 cites W2001128085 @default.
• W2019919549 cites W2007937365 @default.
• W2019919549 cites W2011742661 @default.
• W2019919549 cites W2012210654 @default.
• W2019919549 cites W2012476238 @default.
• W2019919549 cites W2013488777 @default.
• W2019919549 cites W2015008140 @default.
• W2019919549 cites W2016482889 @default.
• W2019919549 cites W2017215712 @default.
• W2019919549 cites W2018909738 @default.
• W2019919549 cites W2019207321 @default.
• W2019919549 cites W2020391390 @default.
• W2019919549 cites W2020549498 @default.
• W2019919549 cites W2021205107 @default.
• W2019919549 cites W2024554425 @default.
• W2019919549 cites W2028049334 @default.
• W2019919549 cites W2028070629 @default.
• W2019919549 cites W2029814382 @default.
• W2019919549 cites W2030545120 @default.
• W2019919549 cites W2032270680 @default.
• W2019919549 cites W2041104332 @default.
• W2019919549 cites W2041551945 @default.
• W2019919549 cites W2042251895 @default.
• W2019919549 cites W2048044619 @default.
• W2019919549 cites W2048569821 @default.
• W2019919549 cites W2051005367 @default.
• W2019919549 cites W2052055789 @default.
• W2019919549 cites W2056984388 @default.
• W2019919549 cites W2058727778 @default.
• W2019919549 cites W2059306644 @default.
• W2019919549 cites W2060055696 @default.
• W2019919549 cites W2061521732 @default.
• W2019919549 cites W2061536117 @default.
• W2019919549 cites W2062364173 @default.
• W2019919549 cites W2064319214 @default.
• W2019919549 cites W2065645571 @default.
• W2019919549 cites W2066941385 @default.
• W2019919549 cites W2067395819 @default.
• W2019919549 cites W2071494270 @default.
• W2019919549 cites W2076669121 @default.
• W2019919549 cites W2076922305 @default.
• W2019919549 cites W2081345111 @default.
• W2019919549 cites W2082672776 @default.
• W2019919549 cites W2085030517 @default.
• W2019919549 cites W2086189182 @default.
• W2019919549 cites W2089775076 @default.
• W2019919549 cites W2094749864 @default.
• W2019919549 cites W2102799325 @default.
• W2019919549 cites W2109425215 @default.
• W2019919549 cites W2116913839 @default.
• W2019919549 cites W2116940203 @default.
• W2019919549 cites W2118864639 @default.
• W2019919549 cites W2121067334 @default.
• W2019919549 cites W2125683452 @default.
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• W2019919549 cites W2159767673 @default.
• W2019919549 cites W2171658465 @default.
• W2019919549 cites W1995744953 @default.
• W2019919549 cites W2030109855 @default.
• W2019919549 cites W2094999225 @default.
• W2019919549 doi "https://doi.org/10.1016/j.ijrmms.2013.05.005" @default.
• W2019919549 hasPublicationYear "2013" @default.
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