Computers and Concrete

  • 제18권2호
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  • Pages.155-163
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  • 2016
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Predicting of compressive strength of recycled aggregate concrete by genetic programming

  • 투고 : 2015.11.04
  • 심사 : 2016.03.09
  • 발행 : 2016.08.25
⟨ 이전 논문 다음 논문 ⟩

초록

This paper, proposes 20 models for predicting compressive strength of recycled aggregate concrete (RAC) containing silica fume by using gene expression programming (GEP). To construct the models, experimental data of 228 specimens produced from 61 different mixtures were collected from the literature. 80% of data sets were used in the training phase and the remained 20% in testing phase. Input variables were arranged in a format of seven input parameters including age of the specimen, cement content, water content, natural aggregates content, recycled aggregates content, silica fume content and amount of superplasticizer. The training and testing showed the models have good conformity with experimental results for predicting the compressive strength of recycled aggregate concrete containing silica fume.

키워드

참고문헌

  1. Ajdukiewicz, A. and Kliszczewicz, A. (2002), "Influence of recycled aggregates on mechanical properties of HS/HPC" Cement Concrete Compos., 24(2), 269-279. https://doi.org/10.1016/S0958-9465(01)00012-9
  2. Bhargava, S., Dulikravich, G.S., Murty, G.S., Agarwal, A. and Colaco, M.J. (2011), "Stress corrosion cracking resistant aluminum alloys: optimizing concentrations of alloying elements and tempering" Mater. Manufact. Process., 26(3), 363-374. https://doi.org/10.1080/10426914.2010.536938
  3. Cakir, O. (2014), "Experimental analysis of properties of recycled coarse aggregate (RCA) concrete with mineral additives" Constr. Build. Mater., 68, 17-25. https://doi.org/10.1016/j.conbuildmat.2014.06.032
  4. Cakir, O. and Sofyanli, O.O. (2015), "Influence of silica fume on mechanical and physical properties of recycled aggregate concrete" HBRC J., 11(2), 157-166. https://doi.org/10.1016/j.hbrcj.2014.06.002
  5. Castelli, M., Vanneschi, L. and Silva, S. (2013), "Prediction of high performance concrete strength using genetic programming with geometric semantic genetic operators" Exp. Syst. Appl., 40(17), 6856-6862. https://doi.org/10.1016/j.eswa.2013.06.037
  6. Chen, L., Kou, C.H. and Ma, S.W. (2014), "Prediction of slump flow of high-performance concrete via parallel hyper-cubic gene-expression programming" Eng. Appl. Artif. Intelligence, 34, 66-74. https://doi.org/10.1016/j.engappai.2014.05.005
  7. Elhakam, A.A., Mohamed, A.E. and Awad, E. (2012), "Influence of self-healing, mixing method and adding silica fume on mechanical properties of recycled aggregates concrete" Constr. Build. Mater., 35, 421-427. https://doi.org/10.1016/j.conbuildmat.2012.04.013
  8. Ferreira, C. (2001), "Gene expression programming: a new adaptive algorithm for solving problems" Complex Syst., 13(2), 87-129.
  9. Ferreira, C. (2006), "Automatically defined functions in gene expression programming" Genetic Systems Programming, 21-56, Springer Berlin Heidelberg.
  10. Gandomi, A.H., Mohammadzadeh, D., Perez-Ordonez, J.L. and Alavi, A.H. (2014), "Linear genetic programming for shear strength prediction of reinforced concrete beams without stirrups" Appl. Soft Comput., 19, 112-120. https://doi.org/10.1016/j.asoc.2014.02.007
  11. Ganguly, S., Datta, S. and Chakraborti, N. (2009), "Genetic algorithm-based search on the role of variables in the work hardening process of multiphase steels" Comput. Mater. Sci., 45(1), 158-166. https://doi.org/10.1016/j.commatsci.2008.01.074
  12. Kara, I.F. (2011), "Prediction of shear strength of FRP-reinforced concrete beams without stirrups based on genetic programming" Adv. Eng. Softw., 42(6), 295-304. https://doi.org/10.1016/j.advengsoft.2011.02.002
  13. Kou, S.C. and Poon, C.S. (2015), "Effect of the quality of parent concrete on the properties of high performance recycled aggregate concrete" Constr. Build. Mater., 77, 501-508. https://doi.org/10.1016/j.conbuildmat.2014.12.035
  14. Koza, J.R. (1995), "Survey of genetic algorithms and genetic programming" Wescon Conference Record, 589-594.
  15. Milani, A.A. and Nazari, A. (2012), "Modeling ductile-to-brittle transition temperature of functionally graded steels by gene expression programming" Int. J. Damage Mech., 21(4), 465-492. https://doi.org/10.1177/1056789511406561
  16. Modani, P.O. and Mohitkar, V.M. (2014), "Self-compacting concrete with recycled aggregate: A solution for sustainable development" Int. J. Civil Struct. Eng., 4(3), 430-440.
  17. Nazari, A. and Riahi, S. (2011), "Prediction split tensile strength and water permeability of high strength concrete containing TiO2 nanoparticles by artificial neural network and genetic programming" Compos. Part B: Eng., 42(3), 473-488. https://doi.org/10.1016/j.compositesb.2010.12.004
  18. Nazari, A., Khalaj, G. and Var, N.D. (2011), "Computational investigations of the impact resistance of Aluminum-Epoxy-Laminated composites" Int. J. Damage Mech., . 1056789511411739 https://doi.org/10.1177/1056789511411739
  19. Ozbay, E., Gesoglu, M. and Guneyisi, E. (2008), "Empirical modeling of fresh and hardened properties of self-compacting concretes by genetic programming" Constr. Build. Mater., 22(8), 1831-1840. https://doi.org/10.1016/j.conbuildmat.2007.04.021
  20. Perez, J.L., Cladera, A., Rabunal, J.R. and Abella, F.M. (2010), "Optimal adjustment of EC-2 shear formulation for concrete elements without web reinforcement using Genetic Programming" Eng. Struct., 32(11), 3452-3466. https://doi.org/10.1016/j.engstruct.2010.07.006
  21. Podgornik, B., Leskovsek, V., Kovacic, M. and Vizintin, J. (2011), "Residual stress field analysis and prediction in nitrided tool steel" Mater. Manufact. Process., 26(9), 1097-1103. https://doi.org/10.1080/10426914.2010.525573
  22. Pouraliakbar, H., Monazzah, A.H., Bagheri, R., Reihani, S.S., Khalaj, G., Nazari, A. and Jandaghi, M.R. (2014), "Toughness prediction in functionally graded Al6061/SiCp composites produced by roll-bonding" Ceramics Int., 40(6), 8809-8825. https://doi.org/10.1016/j.ceramint.2014.01.103
  23. Saridemir, M. (2010), "Genetic programming approach for prediction of compressive strength of concretes containing rice husk ash" Constr. Build. Mater., 24(10), 1911-1919. https://doi.org/10.1016/j.conbuildmat.2010.04.011
  24. Saridemir, M. (2011), "Empirical modeling of splitting tensile strength from cylinder compressive strength of concrete by genetic programming" Exp. Syst. Appl., 38(11), 14257-14268.
  25. Saridemir, M. (2014), "Effect of specimen size and shape on compressive strength of concrete containing fly ash: Application of genetic programming for design" Mater. Des., 56, 297-304. https://doi.org/10.1016/j.matdes.2013.10.073
  26. Shahria Alam, M., Slater, E. and Muntasir Billah, A.H.M. (2012), "Green concrete made with RCA and FRP scrap aggregate: Fresh and hardened properties" J. Mater. Civil Eng., 25(12), 1783-1794.
  27. Sonebi, M. and Cevik, A. (2009), "Genetic programming based formulation for fresh and hardened properties of self-compacting concrete containing pulverised fuel ash" Constr. Build. Mater., 23(7), 2614-2622. https://doi.org/10.1016/j.conbuildmat.2009.02.012
  28. Tanyildizi, H. and Cevik, A. (2010), "Modeling mechanical performance of lightweight concrete containing silica fume exposed to high temperature using genetic programming" Constr. Build. Mater., 24(12), 2612-2618. https://doi.org/10.1016/j.conbuildmat.2010.05.001
  29. Ulloa, V.A., Garcia-Taengua, E., Pelufo, M.J., Domingo, A. and Serna, P. (2013), "New views on effect of recycled aggregates on concrete compressive strength" ACI Mater. J., 110(6).
  30. Xie, J.H., Guo, Y.C., Liu, L.S. and Xie, Z.H. (2015), "Compressive and flexural behaviours of a new steel-fibre-reinforced recycled aggregate concrete with crumb rubber" Constr. Build. Mater., 79, 263-272. https://doi.org/10.1016/j.conbuildmat.2015.01.036
  31. www.gepsoft.com

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