TY - JOUR ID - 2017 TI - Comparison of ANFIS and ANN for Estimation of Thermal Conductivity Coefficients of Construction Materials JO - Scientia Iranica JA - SCI LA - en SN - 1026-3098 AU - ÖZEL, Cengiz AU - TOPSAKAL, Alper AD - Suleyman Demirel University, Faculty of Technology, Department of Civil Engineering, 32260 Isparta TURKEY AD - Suleyman Demirel University, Graduate School of Natural And Applied Sciences, Department of Construction Education, 32260 Isparta TURKEY Y1 - 2015 PY - 2015 VL - 22 IS - 6 SP - 2001 EP - 2011 KW - construction materials KW - thermal conductivity coefficients KW - Artificial Neural Networks KW - ANFIS KW - modeling DO - N2 - The determination of the thermal conductivity coefficient of construction materials is very important in terms of fulfilling the condition of comfort, durability of construction materials, the economy of country and individual. In this study, linear regression, adaptive neural based fuzzy inference system (ANFIS), and artificial neural networks (ANN) models were developed to estimate the thermal conductivity coefficient values fromthe surface density (dry specific gravity/thickness) and the unit weight of construction materials. Validations of the developed models were investigated by statistical analysis. In predictive models, while the lowest determination coefficient (R2) and the highest root mean square error (RMSE) were obtained from linear regression, the highest R2 and lowest RMSE were obtained from the ANFIS model. The results of the ANN model according to results of linear regression, while R2 increased by approximately 6%, RMSE decreased by 30-39%. The results of the ANFIS model revealed that while R2 increased by approximately 12%, RMSE decreased by 59-71%. As a result, it is suggested that together the surface density and unit weight with ANFIS, the most appropriate method in the used methods, can be used as an alternative approach to estimate the value of thermal conductivity. UR - https://scientiairanica.sharif.edu/article_2017.html L1 - https://scientiairanica.sharif.edu/article_2017_08795247be857eb34d04d8168ae91c56.pdf ER -