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


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

NEURAL COMPUTING & APPLICATIONS, vol.19, no.4, pp.507-513, 2010 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 19 Issue: 4
  • Publication Date: 2010
  • Doi Number: 10.1007/s00521-009-0292-9
  • Journal Name: NEURAL COMPUTING & APPLICATIONS
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.507-513

Abstract

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)).