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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 109
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
Paper 37

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
2Department of Information Technology, Abu Dhabi University, United Arab Emirates

Full Bibliographic Reference for this paper
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.

Summary
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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