Prediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic
Küçük Resim Yok
Tarih
2009
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
ELSEVIER SCI LTD
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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 oil compressive strength of concrete. (C) 2008 Elsevier Ltd. All rights reserved.
Açıklama
Anahtar Kelimeler
Compressive strength, Slag, Artificial neural networks, Fuzzy logic
Kaynak
CONSTRUCTION AND BUILDING MATERIALS
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
23
Sayı
3
