An artificial neural network model to predict the thermal properties of concrete using different neurons and activation functions
Yükleniyor...
Dosyalar
Tarih
2019-04-01
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Hindawi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Attribution-ShareAlike 3.0 United States
Attribution-ShareAlike 3.0 United States
Özet
Growing 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.
Açıklama
Anahtar Kelimeler
Benchmarking, Chemical Activation, Concrete Testing, Concretes, Energy Efficiency, Energy Utilization, Forecasting, Neurons, Specific Heat, Thermal Conductivity
Kaynak
WoS Q Değeri
Q4
Scopus Q Değeri
Q2
Cilt
2019
Sayı
Künye
Fidan, 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/10.1155/2019/3831813
