Computers and Concrete

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

An integrated approach for optimum design of HPC mix proportion using genetic algorithm and artificial neural networks

  • Received : 2007.11.13
  • Accepted : 2009.06.15
  • Published : 2009.06.25
⟨ Previous Next ⟩

Abstract

This study aims to develop a cost-based high-performance concrete (HPC) mix optimization system based on an integrated approach using artificial neural networks (ANNs) and genetic algorithms (GA). ANNs are used to predict the three main properties of HPC, namely workability, strength and durability, which are used to evaluate fitness and constraint violations in the GA process. Multilayer back-propagation neural networks are trained using the results obtained from experiments and previous research. The correlation between concrete components and its properties is established. GA is employed to arrive at an optimal mix proportion of HPC by minimizing its total cost. A system prototype, called High Performance Concrete Mix-Design System using Genetic Algorithm and Neural Networks (HPCGANN), was developed in MATLAB. The architecture of the proposed system consists of three main parts: 1) User interface; 2) ANNs prediction models software; and 3) GA engine software. The validation of the proposed system is carried out by comparing the results obtained from the system with the trial batches. The results indicate that the proposed system can be used to enable the design of HPC mix which corresponds to its required performance. Furthermore, the proposed system takes into account the influence of the fluctuating unit price of materials in order to achieve the lowest cost of concrete, which cannot be easily obtained by traditional methods or trial-and-error techniques.

Keywords

References

  1. Bentz, D.P. (2007), "A virtual rapid chloride permeability test", Cement Concrete Comp., 29(10), 723-731. https://doi.org/10.1016/j.cemconcomp.2007.06.006
  2. Cheng, Y. and Li, F.D. (1997) "Multiobjective optimization design with Pareto genetic algorithm", J. Struct. Eng., 123, 1252-1261. https://doi.org/10.1061/(ASCE)0733-9445(1997)123:9(1252)
  3. Feng, N., Feng, X., Hao, T. and Xing, F. (2002), "Effect of ultrafine mineral powder on charge passed of the concrete", Cement Concrete Res., 32, 623-627. https://doi.org/10.1016/S0008-8846(01)00731-1
  4. Goldberg, D.E. (1989), Genetic algorithms in search, optimization, and machine learning, Addison-Wesley, Reading.
  5. Graybeal, B. and Tanesi, J. (2007), "Durability of an ultrahigh-performance concrete", J. Mater. Civil Eng., 19, 848-854. https://doi.org/10.1061/(ASCE)0899-1561(2007)19:10(848)
  6. Holland, J.H. (1992), Adaptation in natural and artificial systems, University of Michigan Press, Michigan.
  7. Houck, C.R., Joines, J. and Kay, M. (1996), "A genetic algorithm for function optimization: A matlab implementation". ACM.
  8. Ji, T., Lin, T. and Lin, X. (2006), "A concrete mix proportion design algorithm based on artificial neural networks", Cement Concrete Comp., 36, 1399-1408. https://doi.org/10.1016/j.cemconres.2006.01.009
  9. Kasperkiewicz, Racz, J. and Dubrawski, A. (1995), "HPC strength prediction using artificial neural networks", J. Comput. Civil Eng., 9, 279-284. https://doi.org/10.1061/(ASCE)0887-3801(1995)9:4(279)
  10. Kuri Morales, A. and Quezada, C.C. (1998), "A Universal eclectic genetic algorithm for constrained optimization", Proceedings 6th European Congress on Intelligent Techniques & Soft Computing, EUFIT'98, 518-522.
  11. Lim, C.H., Yoon, Y.S. and Kim, J.H. (2004), "Genetic algorithm in mix proportioning of high-performance concrete", Cement Concrete Res., 34, 409-420. https://doi.org/10.1016/j.cemconres.2003.08.018
  12. Metha, P.K. and Aïtcin, P.C. (1990), "Principles underlying the production of high-performance concrete", Cement Concrete Aggr., 12, 70-78. https://doi.org/10.1520/CCA10274J
  13. Michalewicz, Z., Dasgupta, D., Riche, R.L. and Schoemauer, M. (1996), "Evolutionary algorithms for constrained engineering problems", Comput. Indust. Eng., 30(4), 851-870. https://doi.org/10.1016/0360-8352(96)00037-X
  14. Oztas, A., Pala, M., Ozbay, E., Kanca, N., Caglar, E. and Bhatti, M.A. (2006), "Predicting the compressive strength and slump of high strength concrete using neural networks", Constr Build Mater., 21, 384-394.
  15. Nimityongskul, P., Plengkham, K. and Piyarakskul, S. (2003), "Techniques and Methods of High-Strength Concrete Producing in Thailand", Proceeding of 1st National Convention on Concrete, Thailand, 7-16.
  16. Nanakorn, P. and Meesomklin, K. (2001), "An adaptive penalty function in genetic algorithms for structural design optimization", Comput. Struct., 79, 2527-2539. https://doi.org/10.1016/S0045-7949(01)00137-7
  17. Powell, D. and Skolnick, M.M. (1993), "Using genetic algorithms in engineering design optimization with nonlinear constraint", (Ed. Kaufmann, M.), Proceedings of the 5th International Conference on Genetic Algorithm, San Mateo.
  18. Shi, C. (2004), "Effect of mixing proportions of concrete on its electrical conductivity and the rapid chloride permeability test (ASTM C1202 or AASHTO 227) results", Cement Concrete Res., 34, 537-545. https://doi.org/10.1016/j.cemconres.2003.09.007
  19. Sirivivatnanon, V. and Cao, H. T. (1998), "Binder dependency of durability properties of HPC", Proceedings of Canadian International Symposium of HPC and Reactive Powder Concrete, Canada.
  20. Smith, A.E. and Coit, D.W. (1997), Constraint handling techniques-penalty functions, Oxford University Press and Institute of Physic Publishing.
  21. Tahir, M.A. (1998), Model for predicting strength development of concrete incorporating fly ash of variable chemical composition and fineness, School of Civil Engineering, Asian Institute of Technology, Pathumthani.
  22. Wee, T.H., Suryavanshi, A.K. and Tin, S.S. (1999), "Influence of aggregate fraction in the mix on the reliability of the rapid chloride permeability test", Cement Concrete Res., 21, 59-72.
  23. Yeh, I.C. (1999), "Design of High-Performance Concrete Mixture Using Neural Networks and Nonlinear Programming", J. Comput. Civil Eng., 13, 36-42. https://doi.org/10.1061/(ASCE)0887-3801(1999)13:1(36)
  24. Yeh, I.C. (2006), "Analysis of strength of concrete using design of experiments and neural networks", J. Mater. Civil Eng., 18, 598-604.
  25. Zhao, T.J., Zhou, Z.H., Zhu, J.Q. and Feng, N.Q. (1998), "An alternating test method for concrete permeability", Cement Concrete Res., 28., 7-12. https://doi.org/10.1016/S0008-8846(97)00212-3

Cited by

  1. Assessment of the CO2 emission and cost reduction performance of a low-carbon-emission concrete mix design using an optimal mix design system vol.25, 2013, https://doi.org/10.1016/j.rser.2013.05.013
  2. The virtual penetration laboratory: new developments for projectile penetration in concrete vol.7, pp.2, 2010, https://doi.org/10.12989/cac.2010.7.2.087
  3. Genetic algorithm in mix proportion design of recycled aggregate concrete vol.11, pp.3, 2013, https://doi.org/10.12989/cac.2013.11.3.183
  4. The high-rate brittle microplane concrete model: Part I: bounding curves and quasi-static fit to material property data vol.9, pp.4, 2012, https://doi.org/10.12989/cac.2012.9.4.293
  5. Polynomial modeling of confined compressive strength and strain of circular concrete columns vol.11, pp.6, 2013, https://doi.org/10.12989/cac.2013.11.6.603
  6. A systematic approach for the development of porous concrete based on axiomatic design theory vol.6, pp.6, 2009, https://doi.org/10.12989/cac.2009.6.6.491
  7. Prediction of the transfer length of prestressing strands with neural networks vol.12, pp.2, 2013, https://doi.org/10.12989/cac.2013.12.2.187
  8. An Optimization System for Concrete Life Cycle Cost and Related CO2 Emissions vol.8, pp.4, 2016, https://doi.org/10.3390/su8040361
  9. Application of artificial neural networks (ANNs) and linear regressions (LR) to predict the deflection of concrete deep beams vol.11, pp.3, 2013, https://doi.org/10.12989/cac.2013.11.3.237
  10. An evolutionary system for the prediction of high performance concrete strength based on semantic genetic programming vol.19, pp.6, 2017, https://doi.org/10.12989/cac.2017.19.6.651
  11. Knowledge-based learning for modeling concrete compressive strength using genetic programming vol.23, pp.4, 2009, https://doi.org/10.12989/cac.2019.23.4.255
  12. Effect of Carbon Pricing on Optimal Mix Design of Sustainable High-Strength Concrete vol.11, pp.20, 2019, https://doi.org/10.3390/su11205827
  13. 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
  14. Artificial intelligence for the compressive strength prediction of novel ductile geopolymer composites vol.28, pp.1, 2009, https://doi.org/10.12989/cac.2021.28.1.055