Comparison between ANFIS and ANN for estimation of the thermal conductivity coefficients of construction materials

Determination of the thermal conductivity coecient of construction materials is very important in terms of fulfilling the condition of comfort, durability of construction materials, and 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 coecient values from the surface density (dry specific gravity/thickness) and unit weight of construction materials. validations of the developed models were investigated by statistical analyses. in the predictive models, while the lowest determination coecient (r^2) and the highest root mean square error (rmse) were obtained from linear regression, the highest r^2 and lowest rmse were obtained from the anfis model. results of the ann model, according to the results of linear regression, showed that while r^2 increased by approximately 6%, rmse decreased by 30-39%. the results of anfis model revealed that while r^2 increased by approximately 12%, rmse decreased by 59-71%. as a result, it is suggested to be, along with surface density and unit weight with anfis which are the most appropriate methods between the used methods, an alternative approach to estimate the value of thermal conductivity.