The Experts below are selected from a list of 117 Experts worldwide ranked by ideXlab platform
C Gokceoglu - One of the best experts on this subject based on the ideXlab platform.
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an application of adaptive neuro fuzzy inference system for estimating the uniaxial compressive strength of certain granitic Rocks from their mineral contents
Expert Systems With Applications, 2013Co-Authors: N Yesiloglugultekin, C Gokceoglu, Ebru Akcapinar Sezer, Hasan BayhanAbstract:The uniaxial compressive strength (UCS) of Rocks is an important intact Rock Parameter, and it is commonly used for various engineering applications. This Parameter is mainly controlled by the mineralogical and textural characteristics of Rocks. In this study, a soft computing method, an adaptive neuro-fuzzy inference system (ANFIS), was employed to estimate UCS from the mineral contents of certain granitic Rocks selected from Turkey; nonlinear multiple regression analysis was then employed to validate these estimations. Five nonlinear multiple regressions and ANFIS models were constructed with three inputs: quartz, orthoclase and plagioclase. To determine the optimal model, various performance indices (R, values account for and root mean square error) were determined, and the model obtained from dataset #3 was selected as the optimal model. The coefficients of correlation for the nonlinear multiple regression and ANFIS models were 0.87 and 0.91, respectively. Thus, both models yielded acceptable results, and the ANFIS is a suitable method for estimating the UCS of Rocks.
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application of fuzzy inference system and nonlinear regression models for predicting Rock brittleness
Expert Systems With Applications, 2010Co-Authors: Saffet Yagiz, C GokceogluAbstract:Brittleness is one of the most crucial Rock features for underground excavation and design considerations in Rock mass. Direct standard testing method for measuring Rock brittleness, the combination of Rock properties rather than only one Rock Parameter have not available yet. Therefore, it is indirectly calculated as a function of some Rock properties such as Rock strength by using various ratios and prediction tools. The aim of this study is to estimate the Rock brittleness by constructing fuzzy inference system and nonlinear regression analysis. For this purpose, a dataset established by utilizing the relevant laboratory Rock tests (i.e., punch penetration, uniaxial compressive strength, Brazilian tensile strength and unit weight of Rock) at the Earth Mechanics Institute of Colorado School of Mines in the USA on the Rock samples assembled from 48 tunnels projects throughout the world. Running the established models, the performance values such as RMSE, VAF, absolute error and coefficient of cross-correlation were computed for developed models. The VAF and RMSE indices were calculated as 89.8% and 2.97 for the nonlinear multiple regression model and 83.1% and 3.82 for fuzzy model, respectively. As a result, these indices revealed that the prediction performance of the nonlinear multiple regression model is higher than that of the fuzzy inference system model. However, it is concluded that both constructed models exhibited a high performance according to the obtained prediction values.
Hasan Bayhan - One of the best experts on this subject based on the ideXlab platform.
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an application of adaptive neuro fuzzy inference system for estimating the uniaxial compressive strength of certain granitic Rocks from their mineral contents
Expert Systems With Applications, 2013Co-Authors: N Yesiloglugultekin, C Gokceoglu, Ebru Akcapinar Sezer, Hasan BayhanAbstract:The uniaxial compressive strength (UCS) of Rocks is an important intact Rock Parameter, and it is commonly used for various engineering applications. This Parameter is mainly controlled by the mineralogical and textural characteristics of Rocks. In this study, a soft computing method, an adaptive neuro-fuzzy inference system (ANFIS), was employed to estimate UCS from the mineral contents of certain granitic Rocks selected from Turkey; nonlinear multiple regression analysis was then employed to validate these estimations. Five nonlinear multiple regressions and ANFIS models were constructed with three inputs: quartz, orthoclase and plagioclase. To determine the optimal model, various performance indices (R, values account for and root mean square error) were determined, and the model obtained from dataset #3 was selected as the optimal model. The coefficients of correlation for the nonlinear multiple regression and ANFIS models were 0.87 and 0.91, respectively. Thus, both models yielded acceptable results, and the ANFIS is a suitable method for estimating the UCS of Rocks.
Saffet Yagiz - One of the best experts on this subject based on the ideXlab platform.
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application of fuzzy inference system and nonlinear regression models for predicting Rock brittleness
Expert Systems With Applications, 2010Co-Authors: Saffet Yagiz, C GokceogluAbstract:Brittleness is one of the most crucial Rock features for underground excavation and design considerations in Rock mass. Direct standard testing method for measuring Rock brittleness, the combination of Rock properties rather than only one Rock Parameter have not available yet. Therefore, it is indirectly calculated as a function of some Rock properties such as Rock strength by using various ratios and prediction tools. The aim of this study is to estimate the Rock brittleness by constructing fuzzy inference system and nonlinear regression analysis. For this purpose, a dataset established by utilizing the relevant laboratory Rock tests (i.e., punch penetration, uniaxial compressive strength, Brazilian tensile strength and unit weight of Rock) at the Earth Mechanics Institute of Colorado School of Mines in the USA on the Rock samples assembled from 48 tunnels projects throughout the world. Running the established models, the performance values such as RMSE, VAF, absolute error and coefficient of cross-correlation were computed for developed models. The VAF and RMSE indices were calculated as 89.8% and 2.97 for the nonlinear multiple regression model and 83.1% and 3.82 for fuzzy model, respectively. As a result, these indices revealed that the prediction performance of the nonlinear multiple regression model is higher than that of the fuzzy inference system model. However, it is concluded that both constructed models exhibited a high performance according to the obtained prediction values.
N Yesiloglugultekin - One of the best experts on this subject based on the ideXlab platform.
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an application of adaptive neuro fuzzy inference system for estimating the uniaxial compressive strength of certain granitic Rocks from their mineral contents
Expert Systems With Applications, 2013Co-Authors: N Yesiloglugultekin, C Gokceoglu, Ebru Akcapinar Sezer, Hasan BayhanAbstract:The uniaxial compressive strength (UCS) of Rocks is an important intact Rock Parameter, and it is commonly used for various engineering applications. This Parameter is mainly controlled by the mineralogical and textural characteristics of Rocks. In this study, a soft computing method, an adaptive neuro-fuzzy inference system (ANFIS), was employed to estimate UCS from the mineral contents of certain granitic Rocks selected from Turkey; nonlinear multiple regression analysis was then employed to validate these estimations. Five nonlinear multiple regressions and ANFIS models were constructed with three inputs: quartz, orthoclase and plagioclase. To determine the optimal model, various performance indices (R, values account for and root mean square error) were determined, and the model obtained from dataset #3 was selected as the optimal model. The coefficients of correlation for the nonlinear multiple regression and ANFIS models were 0.87 and 0.91, respectively. Thus, both models yielded acceptable results, and the ANFIS is a suitable method for estimating the UCS of Rocks.
Ebru Akcapinar Sezer - One of the best experts on this subject based on the ideXlab platform.
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an application of adaptive neuro fuzzy inference system for estimating the uniaxial compressive strength of certain granitic Rocks from their mineral contents
Expert Systems With Applications, 2013Co-Authors: N Yesiloglugultekin, C Gokceoglu, Ebru Akcapinar Sezer, Hasan BayhanAbstract:The uniaxial compressive strength (UCS) of Rocks is an important intact Rock Parameter, and it is commonly used for various engineering applications. This Parameter is mainly controlled by the mineralogical and textural characteristics of Rocks. In this study, a soft computing method, an adaptive neuro-fuzzy inference system (ANFIS), was employed to estimate UCS from the mineral contents of certain granitic Rocks selected from Turkey; nonlinear multiple regression analysis was then employed to validate these estimations. Five nonlinear multiple regressions and ANFIS models were constructed with three inputs: quartz, orthoclase and plagioclase. To determine the optimal model, various performance indices (R, values account for and root mean square error) were determined, and the model obtained from dataset #3 was selected as the optimal model. The coefficients of correlation for the nonlinear multiple regression and ANFIS models were 0.87 and 0.91, respectively. Thus, both models yielded acceptable results, and the ANFIS is a suitable method for estimating the UCS of Rocks.
