Prediction of Fresh and Hardened Properties of Self-Consolidating Concrete Using Neurofuzzy Approach
Publication: Journal of Materials in Civil Engineering
Volume 21, Issue 11
Abstract
Self-consolidating concrete (SCC) developed in Japan in the late 80s has enabled the construction industry to reduce demand on the resources, improve the work conditions and also reduce the impact on the environment by elimination of the need for compaction. This investigation aimed at exploring the potential use of the neurofuzzy (NF) approach to model the fresh and hardened properties of SCC containing pulverised fuel ash (PFA) as based on experimental data investigated in this paper. Twenty six mixes were made with water-to-binder ratio ranging from 0.38 to 0.72, cement content ranging from 183 to , dosage of PFA ranging from 29 to , and percentage of superplasticizer, by mass of powder, ranging from 0 to 1%. Nine properties of SCC mixes modeled by NF were the slump flow, JRing combined to the Orimet, JRing combined to cone, V-funnel, L-box blocking ratio, segregation ratio, and the compressive strength at 7, 28, and 90 days. These properties characterized the filling ability, the passing ability, the segregation resistance of fresh SCC, and the compressive strength. NF model is constructed by training and testing data using the experimental results obtained in this study. The results of NF models were compared with experimental results and were found to be quite accurate. The proposed NF models offers useful modeling approach of the fresh and hardened properties of SCC containing PFA.
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Acknowledgments
This research was supported by the Engineering and Physical Sciences Research Council (EPSRC) of United Kingdom Grant No. EPSRC-GBGR/R75229/01 (M. Sonebi), and Gaziantep University Project Research Unit (A. Cevik).
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© 2009 ASCE.
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Received: May 15, 2008
Accepted: Apr 21, 2009
Published online: Oct 15, 2009
Published in print: Nov 2009
Notes
Note. Associate Editor: Carl Liu
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