Cost Minimum Proportioning of Non-Slump Concrete Mix Using Genetic Algorithms

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Abstract:

This paper presents a generalized formulation for determining the optimal quantity of the materials used to produce Non-Slump Concrete with minimum possible cost. The proposed problem is formulated as a nonlinear constrained optimization problem. The proposed problem considers cost of the individual constituent material costs as well as the compressive strength and other requirement. The optimization formulation is employed to minimize the cost function of the system while constraining it to meet the compressive strength and workability requirement. The results demonstrate the efficiency of the proposed approach to reduce the cost as well as to satisfy the above requirement.

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Periodical:

Advanced Materials Research (Volumes 468-471)

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50-54

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February 2012

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] J. Sobhani, M. Najimi, A. R. Pourkhorshidi, and T. Parhizkar,Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models, Construction and Building Materials, vol. 24, (2010) 709-718.

DOI: 10.1016/j.conbuildmat.2009.10.037

Google Scholar

[2] ACI-211.3, Guide for selecting proportions for no-slump concrete. Farmington Hills (MI): American Concrete Institute; 2002, 2002.

Google Scholar

[3] R. Mannonen, "Proportioning in the ready-mix industry and the benefits of a mix design program," in In: ERMCO-95, Proceedings of the XIth European Ready Mixed Concrete Congress, Istanbul, 1995, pp.177-184.

Google Scholar

[4] J. Kasperkiewicz,Optimization of Concrete Mix Using a Spreadsheet Package, ACI Material Journal, vol. 91, (1995) 551-559.

Google Scholar

[5] I. C. Yeh,Design of High-Performance Concrete Mixture Using Neural Networks and Nonlinear Programming, Journal of Computing in Civil Engineering, vol. 13, (1999) 36-42.

DOI: 10.1061/(asce)0887-3801(1999)13:1(36)

Google Scholar

[6] C. J. Ke, Q. Hu, P. Jiang, and L. Zhang,Optimum Autoclaved Cement Concrete Mix Design Based on Flexural Strength, in Manufacturing Process Technology, Pts 1-5. vol. 189-193, Z. Y. Jiang, S. Q. Li, J. M. Zeng, X. P. Liao, and D. G. Yang, Eds., ed Stafa-Zurich: Trans Tech Publications Ltd, 2011, pp.676-679.

DOI: 10.4028/www.scientific.net/amr.189-193.676

Google Scholar

[7] L. Fan, L. Zhang, and F. H. Li,Comparative study on HPC mix design methods, in High Performance Structures and Materials Engineering, Pts 1 and 2. vol. 217-218, M. Zhou, Ed., ed Stafa-Zurich: Trans Tech Publications Ltd, 2011, pp.175-180.

DOI: 10.4028/www.scientific.net/amr.217-218.175

Google Scholar

[8] Y. D. Wang and X. C. Fan,Experimental Research on Physical and Mechanical Properties of Steel Fiber High-strength Concrete, in Advances in Building Materials, Pts 1-3. vol. 168-170, L. J. Li, Ed., ed Stafa-Zurich: Trans Tech Publications Ltd, 2011, pp.1061-1064.

DOI: 10.4028/www.scientific.net/amr.168-170.1061

Google Scholar

[9] X. S. Song and G. Q. Xu,Optimum Study and Engineering Application of Mixing Ratio for C50 Concrete, in Advances in Building Materials, Pts 1-3. vol. 168-170, L. J. Li, Ed., ed Stafa-Zurich: Trans Tech Publications Ltd, 2011, pp.537-540.

DOI: 10.4028/www.scientific.net/amr.168-170.537

Google Scholar

[10] R. Perera and F. B. Varona,Flexural and Shear Design of FRP Plated RC Structures Using a Genetic Algorithm, Journal of Structural Engineering, vol. 135, (2009) 1418-1429.

DOI: 10.1061/(asce)0733-9445(2009)135:11(1418)

Google Scholar

[11] D. Goldberg, Genetic algorithms in search, optimization and machine learning. New York: Addison Wesley; 1989. New York: Addison Wesley, 1989.

Google Scholar

[12] C. Lian, Y. Zhug, and S. Beecham,Modelling Pervious Concrete under Compression Loading - A Discrete Element Approach, in Advances in Building Materials, Pts 1-3. vol. 168-170, L. J. Li, Ed., ed Stafa-Zurich: Trans Tech Publications Ltd, 2011, pp.1590-1600.

DOI: 10.4028/www.scientific.net/amr.168-170.1590

Google Scholar

[13] Y. I-Cheng,Computer-aided design for optimum concrete mixtures, Cement and Concrete Composites, vol. 29, (2007) 193-202.

DOI: 10.1016/j.cemconcomp.2006.11.001

Google Scholar