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Öğe An experimental investigation of the effect of thermophysical properties on time lag and decrement factor for building elements(Gazi University, 2020-06-01) Oktay, Hasan; Yumrutaş, Recep; Argunhan, ZekiThe time lag (TL) and decrement factor (DF) are essential for the heat storage capabilities of building elements, which strictly depend on the thermophysical properties of the elements. Many investigations are presented in literature arguing to find the influence of each thermophysical property on TL and DF by keeping the other properties constant. This study aims to investigate the effect of each property on TL and DF, utilizing relationships between the measurement values of the thermophysical properties of wall materials. Therefore, first, 132 new concrete wall samples were produced, and their thermophysical properties were tested. Secondly, TL and DF values for each building element are computed from the solution of the problem by Complex Finite Fourier Transform (CFFT) technique. Finally, a multivariate regression analysis has been performed, and the variations of each thermophysical property versus TL and DF are presented, and also the findings are compared with literature. The results show that each property alone (keeping the other properties constant) is not adequate to identify the thermal inertia and thermal performance of a wall element. Besides, 87.3 % decrease in thermal diffusivity corresponds to 6.03 h increase in the value of TL and 88.8 % decrease in value of DF; respectively, for W1 wall assembly.Öğe Prediction of the thermal conductivity of lightweight building materials utilizing backpropagation neural network method(Fırat Üniversitesi, 2015) Oktay, Hasan; Polat, Süleyman; Fidan, ŞehmusThe 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.Öğe Experimental and articial neural network based studies on thermal conductivity of lightweight building materials(European Journal of Technique (EJT), 2017-04-01) Oktay, Hasan; Fidan, Şehmus; Sevim, Davut; Polat, SüleymanThe 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 structures of which the mechanical properties are known; by using Levenberg-Marquardt training algorithm based artificial neural network (ANNs) method for energy efficient buildings. The produced samples are cement based and have relatively high insulation properties for energy efficient buildings. In this regard, 102 new concrete 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 experimental results and the ANN predicted results. It can be concluded that thermal performance of lightweight materials could be predicted with high accuracy using artificial neural network approach.Öğe Investigation of the thermal and acoustic performance of perlite- based building materials(European Journal of Technique (EJT), 2016-12) Oktay, Hasan; Argunhan, Zeki; Doğmuş, RecepPerlite is a siliceous volcanic glass that provides heat and sound insulation, whose volume can expand substantially under the effect of heat. Perlite ore is one of the most important mineral resources for Turkey where holds a large portion reserves in the world. Evaluation of perlite in building industry, which has advantages in terms of heat and sound insulation, will make an important contribution to the national economy. In this context, experimental investigations are performed for obtaining new concrete types with relatively high strength, low density and good thermal and acoustic properties for energy efficient buildings. For this purpose, 6 sets and different types of concrete samples were prepared with a constant watercement ratio, and normal aggregates replaced by expanded perlite aggregates at different volume fractions such as 10%, 20%, 30%, 40%, 50% and 60% of the total aggregate volume. Mechanical and thermal tests were all conducted and the hot disk method was used to establish thermal property values of concrete samples. The results of the experimental studies show that the compressive strength and density decreases, while highly increases the heat and sound insulation features with increasing in perlite content. As a result, it was found out that the reductions in thermal conductivity [Wm–1K–1] and ultrasonic pulse velocity [km/s] of the produced samples reached to 75% and 35%, respectively.
