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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

doi:10.4203/ccp.109.37

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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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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	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 doi:10.4203/ccp.109.37 purchase the full-text of this paper 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. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £50 +P&P)
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