Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete

Oezcan F., ATİŞ C. D., KARAHAN O., Uncuoglu E., Tanyildizi H.

Advances in Engineering Software, vol.40, no.9, pp.856-863, 2009 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 40 Issue: 9
  • Publication Date: 2009
  • Doi Number: 10.1016/j.advengsoft.2009.01.005
  • Journal Name: Advances in Engineering Software
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.856-863
  • Keywords: Silica fume, Concrete, Compressive strength, Neural networks, Fuzzy logic, SULFATE RESISTANCE, CEMENT, MORTARS, METAKAOLIN
  • Kayseri University Affiliated: No


In this study, an artificial neural network (ANN) and fuzzy logic (FL) study were developed to predict the compressive strength of silica fume concrete. A data set of a laboratory work, in which a total of 48 concretes were produced, was utilized in the ANNs and FL study. The concrete mixture parameters were four different water-cement ratios, three different cement dosages and three partial silica fume replacement ratios. Compressive strength of moist cured specimens was measured at five different ages. The obtained results with the experimental methods were compared with ANN and FL results. The results showed that ANN and FL can be alternative approaches for the predicting of compressive strength of silica fume concrete. © 2009 Elsevier Ltd. All rights reserved.