Prediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic

✍ Scribed by Mustafa Sarıdemir; İlker Bekir Topçu; Fatih Özcan; Metin Hakan Severcan

• Publisher: Elsevier Science
• Year: 2009
• Tongue: English
• Weight: 264 KB
• Volume: 23
• Category: Article
• ISSN: 0950-0618
• DOI: 10.1016/j.conbuildmat.2008.07.021

✦ Synopsis

In this study, artificial neural networks and fuzzy logic models for prediction of long-term effects of ground granulated blast furnace slag on compressive strength of concrete under wet curing conditions have been developed. For purpose of constructing these models, 44 different mixes with 284 experimental data were gathered from the literature. The data used in the artificial neural networks and fuzzy logic models are arranged in a format of five input parameters that cover the age of specimen, Portland cement, ground granulated blast furnace slag, water and aggregate, and output parameter which is 3, 7, 14, 28, 63, 90, 119, 180 and 365-day compressive strength. In the models of the training and testing results have shown that artificial neural networks and fuzzy logic systems have strong potential for prediction of long-term effects of ground granulated blast furnace slag on compressive strength of concrete.