Prediction of the Shear Strength of Glass Fiber-Reinforced Clay Soil by Adaptive Neuro-Fuzzy Inference System (ANFIS)

Authors : Ahmetcan SUNGUR, Mehmet Fatih YAZICI, Nilay KESKİN

Pages : 1255-1264

Doi:10.31202/ecjse.1133184

View : 21 | Download : 8

Publication Date : 2022-12-31

Article Type : Research Paper

Abstract :The objective of this study is to estimate the shear strength of glass fiber reinforced clay soil using ANFIS. For this purpose, specimens with different water contents insert ignore into journalissuearticles values(13%, 15% and 17%); and different glass fiber addition ratios insert ignore into journalissuearticles values(0%, 1%, 1.5% and 2%); were prepared. The ANFIS models were created using the shear strength insert ignore into journalissuearticles values(τ); data obtained by direct shear tests on the prepared specimens. To create the best fitting ANFIS model in the current study, 75%, 77%, 80%, and 83% of the data for training and 25%, 23%, 20%, and 17% of the data for testing were used, respectively. However, to estimate the shear strength in each ANFIS model, the normal stress insert ignore into journalissuearticles values(σ);, glass fiber content insert ignore into journalissuearticles values(Fc);, and water content insert ignore into journalissuearticles values(ω); are considered as input parameters. Statistical parameters such as root mean square error insert ignore into journalissuearticles values(RMSE);, regression coefficient insert ignore into journalissuearticles values(R2);, root square error insert ignore into journalissuearticles values(RSE);, and mean absolute error insert ignore into journalissuearticles values(MAE); were also calculated to determine the success rates of the ANFIS models. Examination of the statistical parameters revealed that the data used 80% for training and 20% for testing provided the best results in estimating the shear strength of the ANFIS model.
Keywords : Yapay Zeka Algoritmaları, ANFIS, Kayma Mukavemeti, Cam Lif, Zeminlerin Güçlendirilmesi