Journal
ADVANCES IN ENGINEERING SOFTWARE
Volume 34, Issue 11-12, Pages 663-669Publisher
ELSEVIER SCI LTD
DOI: 10.1016/S0965-9978(03)00102-9
Keywords
neural networks; modelling; prediction; concrete workability; metakaolin; fly ash
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This paper details the development of neural network models that provide effective predictive capability in respect of the workability of concrete incorporating metakaolin (MK) and fly ash (FA). The predictions produced reflect the effect of graduated variations in pozzolanic replacement in Portland cement (PC) of up to 15% MK and 40% FA. The results show that the models are reliable and accurate and illustrate how neural networks can be used to beneficially predict the workability parameters of slump, compacting factor and Vebe time across a wide range of PC-FA-MK compositions. (C) 2003 Elsevier Ltd. All rights reserved.
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