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Öğe Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete(ELSEVIER SCI LTD, 2009) Oezcan, Fatih; Atis, Cengiz D.; Karahan, Okan; Uncuoglu, Erdal; Tanyildizi, HarunIn 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. (C) 2009 Elsevier Ltd. All rights reserved.Öğe Prediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic(ELSEVIER SCI LTD, 2009) Saridemir, Mustafa; Topcu, Ilker Bekir; Oezcan, Fatih; Severcan, Metin HakanIn 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.