DOI QR코드

DOI QR Code

Genetic algorithm in mix proportion design of recycled aggregate concrete

  • Park, W.J. (Sustainable Building Research Centre, Hanyang University) ;
  • Noguchi, T. (Faculty of Engineering (Architecture), the University of Tokyo) ;
  • Lee, H.S. (School of Architecture, Hanyang University)
  • Received : 2012.09.01
  • Accepted : 2013.01.03
  • Published : 2013.03.25

Abstract

To select a most desired mix proportion that meets required performances according to the quality of recycled aggregate, a large number of experimental works must be carried out. This paper proposed a new design method for the mix proportion of recycled aggregate concrete to reduce the number of trial mixes. Genetic algorithm is adapted for the method, which has been an optimization technique to solve the multi-criteria problem through the simulated biological evolutionary process. Fitness functions for the required properties of concrete such as slump, density, strength, elastic modulus, carbonation resistance, price and carbon dioxide emission were developed based on statistical analysis on conventional data or adapted from various early studies. Then these fitness functions were applied in the genetic algorithm. As a result, several optimum mix proportions for recycled aggregate concrete that meets required performances were obtained.

Keywords

Acknowledgement

Supported by : Ministry of Land, Transport and Maritime Affairs

References

  1. Wang, H.Y., Hwang, C.L. and Yeh, S.T. (2012), "An approach of using ideal grading curve and coating paste thickness to evaluate the performances of concrete-(1) Theory and formulation", Comput. Concrete, 10(1), 19-33. https://doi.org/10.12989/cac.2012.10.1.019
  2. Wang, H.Y., Hwang, C.L. and Yeh, S.T. (2012), "An approach of using ideal grading curve and coating paste thickness to evaluate the performances of concrete-(2) Experimental work", Comput. Concrete, 10(1), 35-47. https://doi.org/10.12989/cac.2012.10.1.035
  3. Shin, S.Y., Lee, I.H. and Zhang, B.T. (2006), "Microarray probe design using $\varepsilon$-multi-objective evolutionary algorithms with thermodynamic criteria", Comput. Sci., 3907, 184-195.
  4. Lim, C.H., Yoon, Y.S. and Kim, J.H. (2004), "Genetic algorithm in mix proportioning of high-performance concrete", Cement Concrete Res., 34(3), 409-420. https://doi.org/10.1016/j.cemconres.2003.08.018
  5. Peng, C.H., Yeh, I.C. and Lien, L.C. (2009), "Modeling strength of high-performance concrete using genetic operation trees with pruning techniques", Comput. Concrete, 6(3), 203-223. https://doi.org/10.12989/cac.2009.6.3.203
  6. Parichatprecha, R. and Nimityongskul, P. (2009), "An integrated approach for optimum design of HPC mix proportion using genetic algorithm and artificial neural networks", Comput. Concrete, 6(3), 253-268. https://doi.org/10.12989/cac.2009.6.3.253
  7. Goldberg, D. (1989), Genetic algorithms in search, optimization and machine learning, Addison-Welsley, Reading, MA.
  8. Holland, J.H. (1975), Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor
  9. Park, W.J. and Noguchi, T. (2011), "A study on mix proportion for recycled aggregate concrete considering reduction of environmental load", ISWA World Congress 2011, Daegu.
  10. Maruyama, I., Kanematsu, M., Noguchi, T. and Tomosawa, F. (2001), "Optimization of mix proportion of concrete under various severe conditions by applying the genetic algorithm", The 3rd (CONSEC'01), Vancouver.
  11. Eduardo M.R. Fairbairn, Marcos M. Silvoso, Romildo D. Toledo Filho, José, L.D. Alves and Nelson F.F. Ebecken (2004), "Optimization of mass concrete construction using genetic algorithms", Comput. Struct., 82(2-3), 281-299. https://doi.org/10.1016/j.compstruc.2003.08.008
  12. Baykasoglu, A., Oztaş, A. and Ozbay, E. (2009), "Prediction and multi-objective optimization of highstrength concrete parameters via soft computing approaches", Expert Syst. Appl., 36(3), 6145-6155. https://doi.org/10.1016/j.eswa.2008.07.017
  13. Yeh, I.C. (2007), "Computer-aided design for optimum concrete mixtures original research article", Cement Concrete Comp., 29(3), 193-202. https://doi.org/10.1016/j.cemconcomp.2006.11.001
  14. Yeh, I.C. and Lien, L.C. (2009), "Knowledge discovery of concrete material using genetic operation trees", Expert Syst. Appl., 36(3), 5807-5812. https://doi.org/10.1016/j.eswa.2008.07.004
  15. Maruyama, I., Noguchi, T. and Kanematsu, M. (2002), "Optimization of concrete mix proportion centered on fresh properties by genetic algorithm", Indian Concrete J., 76, 567-573.
  16. Noguchi, T., Maruyama, I. and Kanematsu, M. (2003), "Performance-based design system for concrete mixture with multi-optimizing genetic algorithm", 11th international congress on the chemistry of cement, Durban.
  17. AIJ (2006), Recommendations for practice of crack control in reinforced concrete buildings (Design and Construction), Architecture Institute of Japan (in Japanese).
  18. Duff A. Abrams (1910), Design of concrete mixture, Structural materials research laboratory, Chicago Lewis Institute, Bulletin-1.
  19. Jones, R. and Kaplan, M.K. (1957), "The effects of coarse aggregate on the mode of failure of concrete in compressive and flexure", Mag. Concrete Res., 9(26), 89-94. https://doi.org/10.1680/macr.1957.9.26.89
  20. Ilker, B. (2007), "Elasticity theory of concrete and prediction of static E-modulus for dam concrete using composite models", Teknik Dergi, 18(1), 4055-4067.
  21. Paulo J.M. Monteiro (1991), "A note on the hirsch model", Cement Concrete Res., 21, 947-950. https://doi.org/10.1016/0008-8846(91)90190-S
  22. Hashin, Z. (1962), "The elastic modulus of hetero-homogeneous materials", J. Appl. Mech., 29(1), 143-150. https://doi.org/10.1115/1.3636446
  23. Kawakami, H. (1994), "Mechanical properties of hardened cement paste", Japan Concrete Inst., 16(1), 497-502 (in Japanese).
  24. Kiyohara, C., Nagamatsu, S., Sato, Y. and Mihashi, H. (2004), "Study on equation for predicting elastic modulus of concrete based on the theory composite material", J. Struct. Constr. Eng., (576), 7-14 (in Japanese).
  25. Izumi, I. (1984), "Reliability design method of cover thickness of reinforcing steel", Japan Concrete Inst., 6, 185-188 (in Japanese).
  26. JSCE (2004), Assessment for environmental impact of concrete (Part 2), Concrete Engineering Series 62, Japan Society of Civil Engineers.
  27. JCI (2010), JCI-TC081A Committee report, Japan Concrete Institute.
  28. JIS A 5021 (2005), Recycled aggregate for concrete-class H, Japan Concrete Institute.
  29. JIS A 5022 (2007), Recycled concrete using recycled aggregate class M, Japan Concrete Institute.
  30. JIS A 5023 (2007), Recycled concrete using recycled aggregate class L, Japan Concrete Institute.

Cited by

  1. Durability characteristics of recycled aggregate concrete vol.47, pp.5, 2013, https://doi.org/10.12989/sem.2013.47.5.701
  2. Experimental investigation on the use of recycled aggregates in producing concrete vol.47, pp.4, 2013, https://doi.org/10.12989/sem.2013.47.4.545
  3. Recycling of geopolymer concrete vol.101, 2015, https://doi.org/10.1016/j.conbuildmat.2015.10.037
  4. Shear strength of RC beams. Precision, accuracy, safety and simplicity using genetic programming vol.14, pp.4, 2014, https://doi.org/10.12989/cac.2014.14.4.479
  5. Optimal Mixture Design of Low-CO2 High-Volume Slag Concrete Considering Climate Change and CO2 Uptake vol.13, pp.1, 2019, https://doi.org/10.1186/s40069-019-0359-7
  6. Performance assessment of nano-Silica incorporated recycled aggregate concrete vol.8, pp.4, 2013, https://doi.org/10.12989/acc.2019.8.4.321
  7. Recycled geopolymer aggregates as coarse aggregates for Portland cement concrete and geopolymer concrete: Effects on mechanical properties vol.236, pp.None, 2020, https://doi.org/10.1016/j.conbuildmat.2019.117571
  8. Durability Evaluation of Concrete with Multiadmixtures under Salt Freeze-Thaw Cycles Based on Surface Resistivity vol.2021, pp.None, 2013, https://doi.org/10.1155/2021/5567873
  9. Investigation on Durability Behaviour and Optimization of Concrete with Triple-Admixtures Subjected to Freeze-Thaw Cycles in Salt Solution vol.2021, pp.None, 2013, https://doi.org/10.1155/2021/5572011
  10. The effect of the new stopping criterion on the genetic algorithm performance vol.27, pp.1, 2021, https://doi.org/10.12989/cac.2021.27.1.063