Prediction of compressive strength of heavyweight concrete by ANN and FL models

BAŞYİĞİT, Celalettin; AKKURT, İskender; KILINÇARSLAN, Şemsettin; Beycioglu, A.

doi:10.1007/s00521-009-0292-9

Prediction of compressive strength of heavyweight concrete by ANN and FL models

BAŞYİĞİT C., AKKURT İ., KILINÇARSLAN Ş., Beycioglu A.

NEURAL COMPUTING & APPLICATIONS, cilt.19, sa.4, ss.507-513, 2010 (SCI-Expanded, Scopus)

Yayın Türü: Makale / Tam Makale
Cilt numarası: 19 Sayı: 4
Basım Tarihi: 2010
Doi Numarası: 10.1007/s00521-009-0292-9
Dergi Adı: NEURAL COMPUTING & APPLICATIONS
Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
Sayfa Sayıları: ss.507-513
Anahtar Kelimeler: Heavyweight concrete, Baryte, Compressive strength, Artificial neural networks, Fuzzy logic, Computer simulation, ARTIFICIAL NEURAL-NETWORKS, FUZZY-LOGIC, MECHANICAL-PROPERTIES, BARITE
Süleyman Demirel Üniversitesi Adresli: Evet

Özet

The compressive strength of heavyweight concrete which is produced using baryte aggregates has been predicted by artificial neural network (ANN) and fuzzy logic (FL) models. For these models 45 experimental results were used and trained. Cement rate, water rate, periods (7-28-90 days) and baryte (BaSO(4)) rate (%) were used as inputs and compressive strength (MPa) was used as output while developing both ANN and FL models. In the models, training and testing results have shown that ANN and FL systems have strong potential for predicting compressive strength of concretes containing baryte (BaSO(4)).