Modeling of Thermal Conductivity of Concrete with Vermiculite Using by Artificial Neural Networks Approaches

Gencel O., Koksal F., ŞAHİN M., Durgun M. Y., Lobland H. E. H., Brostow W.

EXPERIMENTAL HEAT TRANSFER, cilt.26, sa.4, ss.360-383, 2013 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 26 Sayı: 4
  • Basım Tarihi: 2013
  • Doi Numarası: 10.1080/08916152.2012.669810
  • Dergi Adı: EXPERIMENTAL HEAT TRANSFER
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.360-383
  • Anahtar Kelimeler: artificial neural networks, concrete, thermal conductivity, vermiculite, numerical simulation, DIFFERENT ANN TECHNIQUES, MULTIHOLED BRICK WALLS, COMPRESSIVE STRENGTH, LIGHTWEIGHT CONCRETE, CRITICAL SUBMERGENCE, FEEDFORWARD NETWORKS, MECHANICAL-BEHAVIOR, ABRASIVE WEAR, PREDICTION, DESIGN
  • Yozgat Bozok Üniversitesi Adresli: Evet

Özet

In this article, the thermal conductivity of concrete with vermiculite is determined and also predicted by using artificial neural networks approaches, namely the radial basis neural network and multi-layer perceptron. In these models, 20 datasets were used. For the training set, 12 datasets (60%) were randomly selected, and the residual datasets (8 datasets, 40%) were selected as the test set. The root mean square error, the mean absolute error, and determination coefficient statistics are used as evaluation criteria of the models, and the experimental results are compared with these models. It is found that the radial basis neural network model is superior to the other models.