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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 Chloride Penetration of Sustainable Self-Consolidating Concrete
O.A. Mohamed1, M. Ati2 and W. Al Hawat1
1Department of Civil Engineering, Abu Dhabi University, United Arab Emirates
O.A. Mohamed, M. Ati, W. Al Hawat, "Using Artificial Neural Networks to Predict Chloride Penetration 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 37, 2015. doi:10.4203/ccp.109.37
Keywords: chloride penetration, self-consolidating concrete, artificial neural network, fly ash.
The purpose of this paper is to present an artificial neural network (ANN) to predict the chloride penetration of sustainable self-consolidating concrete (SCC) mixes. The ability of concrete to resist chloride penetration is typically measured using a rapid chloride penetration (RCP) test. ANN models were developed by controlling the critical parameters affecting chloride penetration to predict the results of the RCP test. The ANN models were developed using various parameters including ratio of water-to-binder (W/B), course aggregate, fine aggregate, fly ash, and silica fume. Data used to train the ANN were obtained from the literature and validated using test data from experiments conducted at Abu Dhabi University.
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