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O.A. Mohamed¹, M. Ati² and O.F. Najm¹

¹Department of Civil Engineering, Abu Dhabi University, United Arab Emirates
²Department of Information Technology, Abu Dhabi University, United Arab Emirates

doi:10.4203/ccp.109.38

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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.

Summary

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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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 38 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 ²Department of Information Technology, Abu Dhabi University, United Arab Emirates doi:10.4203/ccp.109.38 purchase the full-text of this paper Full Bibliographic Reference for this paper 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. Summary 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. 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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