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PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON SOFT COMPUTING TECHNOLOGY IN CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING
Edited by: Y. Tsompanakis, J. Kruis and B.H.V. Topping
Using Artificial Neural Networks to Predict the Compressive Strength of Sustainable Self-Consolidating Concrete
O.A. Mohamed1, M. Ati2 and O.F. Najm1
1Department of Civil Engineering, Abu Dhabi University, United Arab Emirates
O.A. Mohamed, M. Ati, O.F. Najm, "Using Artificial Neural Networks to Predict the Compressive Strength of Sustainable Self-Consolidating Concrete", in Y. Tsompanakis, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Fourth International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 38, 2015. doi:10.4203/ccp.109.38
Keywords: self-consolidating-concrete, sustainable concrete, water-cement-ratio, fly ash, silica fume, artificial neural networks.
Self-consolidating concrete (SCC) in which significant amount of Portland cement is replaced with fly ash and silica fume is gaining popularity. The process of manufacturing cement is known to contribute significantly to the emission of carbon dioxide into the atmosphere. Therefore, a concrete mix in which significant amount of cement is replaced with a sustainable alternative is known as sustainable concrete.
The purpose of this paper is to present an artificial neural network (ANN) to predict the compressive strength attainable by concrete mixes. The fundamental ANN parameters considered to affect the compressive strength include the water-to-binder ratio, the amounts of high range water reducer, silica fume, fly ash, course aggregate, and fine aggregate. A set of data from the literature is used to train the ANN and the results are validated using data produced in the laboratory by the investigators.
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