Oktay, HasanPolat, SüleymanFidan, Şehmus2021-11-172021-11-172015Oktay, H., Polat, S., Fidan, Ş. (2015). Prediction of the thermal conductivity of lightweight building materials utilizing backpropagation neural network method. International Conference on Advances and Innovations in Engineering (ICAIE), 10-12 May 2015, Elazığ, Turkey9786056757525https://hdl.handle.net/20.500.12402/3997The growing concern about energy consumption of heating and cooling of buildings has led to a demand for improved thermal performances of building materials. In this study, an experimental investigation is performed to predict the thermal insulation properties of wall and roof structures of which the mechanical properties are known, by using backpropagation artificial neural network (ANNs) method. The produced samples are cement based and have relatively high insulation properties for energy efficient buildings. In this regard, 102 new samples and their compositions are produced and their mechanical and thermal properties are tested in accordance with ASTM and EN standards. Then, comparisons have been made between the determined thermal conductivity of the newly produced structures, which are obtained from experimental method and ANN method that uses mechanical properties as input parameters. From the test results, since the percentage errors in the thermal conductivity values between experimental data and neural network prediction vary from - 1.09% to 6.4%, It can be concluded that the prediction of the artificial neural network has proceed in the correct manner.eninfo:eu-repo/semantics/openAccessAttribution-NonCommercial-ShareAlike 3.0 United StatesConcreteThermal ConductivityMechanical PropertiesBack-PropagationEnergy Efficient BuildingConference Object