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Analytic Formulae for Concrete Mix Design Based on Experimental Data Base and Predicting the Concrete Behavior Using ANN Technique
Mostafa Abdeen, Hossam Hodhod
Pages - 368 - 386     |    Revised - 30-11-2010     |    Published - 20-12-2010
Volume - 4   Issue - 5    |    Publication Date - December 2010  Table of Contents
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KEYWORDS
Cement type, Concrete mix proportions, Concrete behavior, Modeling, Artificial Neural Network
ABSTRACT
The Local Egyptian practice in producing concrete for different structural applications is based on the known properties of cement. Cement has been produced locally under the Egyptian standards ES 372, 373 and 584 for ordinary, rapid hardening and sulphate resisting types. In 2007, the Egyptian standards issued ES 4756 that adopted the European standard EN 197 for producing cement. This resulted in new types of cements to replace the types that local construction companies used to apply for decades. Many doubts appeared about whether the rules applied for concrete mix proportioning are still valid. In the current research, an experimental investigation of concrete properties is made using two of the locally most common types of cements CEM I 32.5 R & CEM I 42.5 N. Slump, compressive strength, rebound number and ultrasonic pulse velocities were investigated for 64 mixes. The main parameters were type of cement, cement content, water content, and fine/coarse aggregate ratio. Data base was established for the mix proportions and corresponding properties. Analytic formulae are proposed for utilizing the collected data base for concrete mix design. Also, using the experimental data base presented in the current study, numerical approach, using one of the artificial intelligence techniques, is adopted to simulate the concrete behavior for different mix proportions. Artificial Neural Network (ANN) technique is developed in the present work to simulate the concrete slump and concrete compressive strength for different mix proportions at different ages for the two types of cement and then predict the concrete behavior for different mix proportions at ages rather than those investigated in the experimental work.
CITED BY (4)  
1 Vengadeshwari, R. S., & Reddy, H. J. Optimum Concrete mix Design using Heuristic Techniques.
2 Salcedo, L. O. G., Zúñiga, A. P. G., Arjona, S. D., & Will, A. L. E. (2012). Red Neuronal Artificial para estimar la resistencia a compresión, en concretos fibro-reforzados con polipropileno [Artificial neural network to predict the compressive strength, in polypropylene fiber-reinforced concrete]. Ventana Informática, (26).
3 Salcedo, L. O. G., Zúñiga, A. P. G., Arjona, S. D., & Will, A. L. E.Exploración con redes neuronales artificiales para estimar la resistencia a la compresión, en concretos fibroreforzados con acero exploring artificial neural networks to estimate compressive strength of steel fiber-reinforced concrete.
4 González Salcedo, L. O., Guerrero Zúñiga, A. P., Delvasto Arjona, S., Will, E., & Luis, A. (2012).Exploring artificial neural networks to estimate compressive strength of steel fiber-reinforced concrete. Ciencia e Ingeniería Neogranadina, 22(1), 19-41.
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Associate Professor Mostafa Abdeen
Faculty of Eng., Cairo University - Egypt
mostafa_a_m_abdeen@hotmail.com
Professor Hossam Hodhod
Faculty of Eng., Cairo University - Egypt