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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 Hafif yapı malzemelerinin ısıl iletkenlik özelliklerinin yapay sinir ağları kullanılarak tahmin edilmesi(Batman Üniversitesi, 2020-06-30) Fidan, Şehmus; Oktay, Hasan; Polat, SüleymanBinaların ısıtılması ve soğutulması için tüketilen enerjinin artmasıyla birlikte ısıl performansı yüksek olan bina malzemelerine olan ihtiyaç günden güne artmaktadır. Bina malzemelerinin ısıl performansı ise direk olarak malzemelerin termofiziksel özellikleri ile değişim göstermektedir. Bu çalışmada, binalarda enerji verimliliğini sağlamak için, uygun mekanik özellikler korunarak yüksek ısı yalıtım özelliğine sahip olan yeni yapı malzemeleri elde etmek amacıyla deneysel ve teorik bir çalışma gerçekleştirilmiştir. Bu amaçla, sabit su-çimento oranında, normal agrega yerine hacimce %10, %20, %30, %40 ve %50 oranlarında pomza, genleştirilmiş perlit ve lastik agregaları kullanılarak çeşitli beton numuneleri hazırlanmıştır. 102 adet beton numunesi farklı bileşimlerde ve değişik malzemeler kullanılarak üretilmiştir. Tüm numunelerin mekanik testleri yapılmış, ısıl iletkenlik özellikleri sıcak disk yöntemi ile ASTM ve EN standartlarına uygun olarak belirlenmiştir. Üretilen numunelerden deneysel olarak elde edilen ısıl iletkenlik özelliği geliştirilen yapay sinir ağı çıkışlarıyla karşılaştırılmış ve sonuçlar incelenmiştir. Geliştirilen yapay sinir ağında sadece mekanik özellikler giriş olarak kullanılmış ve malzemelerin ısıl iletkenlik ile ilişkisi araştırılmıştır. Yapay sinir ağı girişi olarak beton tipi, agrega oranı, yoğunluk, basma dayanımı, porozite ve ısıl iletkenlik olarak belirlenmiştir. Çıktılar karşılaştırıldığında, bulunan sonuçların birbirleriyle uyumlu olduğu ve hafif betonlara ait ısıl iletkenlik değeri %-1.09 ile %6,4 arasında bir hata ile tahmin edilmesinin kabul edilebilir olduğu görülmüştür.Öğe Investigation of mechanical properties of lightweight concretes with different lightweight aggregates(Dicle University, 2019) Oktay, Hasan; Aydın, Hüseyin; Işık, Mehmet Zerrakki; Argunhan, ZekiEngineers have to know mechanical characteristics of lightweight concretes which are better than normal concretes. In this study, experimental investigations are performed for obtaining new concrete types to investigate the effect of lightweight aggregates on mechanical properties and to evaluate the relationships between the measured values of these properties. For this purpose, different types of concrete samples were prepared with a constant w/c, and normal aggregates replaced by lightweight aggregates such as pumice, expanded perlite and rubber aggregates at different volume fractions such as 10 %, 20 %, 30 %, 40 % and 50 % of the total aggregate volume. 102 samples were produced, and their mechanical characteristics were tested in accordance with ASTM and EN standards. Moreover, in order to evaluate possible correlations among the tested properties, a multivariate regression analysis was performed. Based on the experimental results, the expressions are presented to determine the relation between the mechanical properties of concrete samples. The results showed that both the compressive strengths and bulk densities of lightweight concrete samples decreased with increasing the (%) percentage of lightweight aggregate content. In addition, the regression analysis results show that if one of the mechanical properties of a structure is known, the other important properties can be calculated easily utilizing those expressions.
