An artificial neural network model to predict the thermal properties of concrete using different neurons and activation functions

Basit Öğe Kaydı
dc.authorid0000-0002-5249-7245en_US
dc.authorid0000-0002-0917-7844en_US
dc.authorid0000-0001-9726-3840en_US
dc.authorid0000-0002-9668-793Xen_US
dc.contributor.authorFidan, Şehmus
dc.contributor.authorOktay, Hasan
dc.contributor.authorPolat, Süleyman
dc.contributor.authorÖztürk, Sarper
dc.date.accessioned2019-07-05T08:09:17Z
dc.date.available2019-07-05T08:09:17Z
dc.date.issued2019-04-01en_US
dc.departmentBatman Üniversitesi Teknik Eğitim Fakültesi Elektrik Eğitimi Bölümüen_US
dc.description.abstractGrowing concerns on energy consumption of buildings by heating and cooling applications have led to a demand for improved insulating performances of building materials. The establishment of thermal property for a building structure is the key performance indicator for energy efficiency, whereas high accuracy and precision tests are required for its determination which increases time and experimental costs. The main scope of this study is to develop a model based on artificial neural network (ANN) in order to predict the thermal properties of concrete through its mechanical characteristics. Initially, different concrete samples were prepared, and their both mechanical and thermal properties were tested in accordance with ASTM and EN standards. Then, the Levenberg-Marquardt algorithm was used for training the neural network in the single hidden layer using 5, 10, 15, 20, and 25 neurons, respectively. For each thermal property, various activation functions such as tangent sigmoid functions and triangular basis functions were used to examine the best solution performance. Moreover, a cross-validation technique was used to ensure good generalization and to avoid overtraining. ANN results showed that the best overall R2 performances for the prediction of thermal conductivity, specific heat, and thermal diffusivity were obtained as 0.996, 0.983, and 0.995 for tansig activation functions with 25, 25, and 20 neurons, respectively. The performance results showed that there was a great consistency between the predicted and tested results, demonstrating the feasibility and practicability of the proposed ANN models for predicting the thermal property of a concrete.en_US
dc.identifier.citationFidan, Ş, Oktay, H., Polat, S., Özturk, S. (2019). An artificial neural network model to predict the thermal properties of concrete using different neurons and activation functions. Advances in Materials Science and Engineering, 2019, pp. 1-13. https://doi.org/10.1155/2019/3831813en_US
dc.identifier.endpage13en_US
dc.identifier.issn1687-8434
dc.identifier.issn1687-8442
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1155/2019/3831813
dc.identifier.urihttps://hdl.handle.net/20.500.12402/2203
dc.identifier.volume2019en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherHindawien_US
dc.relation.isversionof10.1155/2019/3831813en_US
dc.relation.journalAdvances in Materials Science and Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-sa/3.0/us/*
dc.subjectBenchmarkingen_US
dc.subjectChemical Activationen_US
dc.subjectConcrete Testingen_US
dc.subjectConcretesen_US
dc.subjectEnergy Efficiencyen_US
dc.subjectEnergy Utilizationen_US
dc.subjectForecastingen_US
dc.subjectNeuronsen_US
dc.subjectSpecific Heaten_US
dc.subjectThermal Conductivityen_US
dc.titleAn artificial neural network model to predict the thermal properties of concrete using different neurons and activation functionsen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
3831813.pdf
Boyut:
2.2 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Teknik Eğitim Fakültesi Elektrik Eğitimi Bölümü Makale Koleksiyonu
Mühendislik-Mimarlık Fakültesi Makine Mühendisliği Bölümü Makale Koleksiyonu
Mühendislik-Mimarlık Fakültesi, Jeoloji Mühendisliği Bölümü, Makale Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu