Predicting the compressive strength of cement mortars containing FA and SF by MLPNN

Kocak Y., GÜLBANDILAR E., Akcay M.

COMPUTERS AND CONCRETE, cilt.15, sa.5, ss.759-770, 2015 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 15 Sayı: 5
  • Basım Tarihi: 2015
  • Doi Numarası: 10.12989/cac.2015.15.5.759
  • Dergi Adı: COMPUTERS AND CONCRETE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.759-770
  • Anahtar Kelimeler: multi-layer perceptron neural network, cement, silica fume, fly ash, compressive strength, ARTIFICIAL NEURAL-NETWORK, FLY-ASH, SILICA FUME, CONCRETE, DURABILITY, STEEL, RATIO
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

In this study, a multi-layer perceptron neural network (MLPNN) prediction model for compressive strength of the cement mortars has been developed. For purpose of constructing this model, 8 different mixes with 240 specimens of the 2, 7, 28, 56 and 90 days compressive strength experimental results of cement mortars containing fly ash (FA), silica fume (SF) and FA+SF used in training and testing for MLPNN system was gathered from the standard cement tests. The data used in the MLPNN model are arranged in a format of four input parameters that cover the FA, SF, FA+SF and age of samples and an output parameter which is compressive strength of cement mortars. In the model, the training and testing results have shown that MLPNN system has strong potential as a feasible tool for predicting 2, 7, 28, 56 and 90 days compressive strength of cement mortars.