Computers and Concrete

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Prediction model for concrete carbonation depth using gene expression programming

  • Received : 2020.08.19
  • Accepted : 2020.11.20
  • Published : 2020.12.25
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Abstract

Concrete can lose its alkalinity by concrete carbonation causing steel corrosion. Thus, the determination of the carbonation depth is necessary. An empirical model is proposed in this research to predict the carbonation depth of concrete using Gene expression programming (GEP). The GEP model was trained and validated using a large and reliable database collected from the literature. The model was developed using the six parameters that predominantly control the carbonation depth of concrete including carbon dioxide CO2 concentration, relative humidity, water-to-cement ratio, maximum aggregate size, aggregate to binder ratio and carbonation period. The model was statistically evaluated and then compared to the Jiang et al. model. A parametric study was finally performed to check the proposed GEP model's sensitivity to the selected input parameters.

Keywords

References

  1. Abdollahzadeh, G., Jahani, E. and Kashir, Z. (2016), "Predicting of compressive strength of recycled aggregate concrete by genetic programming", Comput. Concrete, 18(2), 155-163. https://doi.org/10.12989/cac.2016.18.2.155.
  2. AL-Bodour, W., Tarawneh, B. and Murad, Y. (2020), "Gene expression programming: A model to predict the standard penetration test N60 value from cone penetration test data", Soil Mechanics and Foundation Engineering.
  3. Atis, C.D. (2003), "Accelerated carbonation and testing of concrete made with fly ash", Constr. Build. Mater., 17(3), 147-152. https://doi.org/10.1016/S0950-0618(02)00116-2.
  4. Azim, I., Yang, J., Iqbal, M.F., Javed, M.F., Nazar, S., Wang, F. and Liu, Q.F. (2020), "Semi-analytical model for compressive arch action capacity of RC frame structures", Struct., 27, 1231-1245. https://doi.org/10.1016/j.istruc.2020.06.011.
  5. Bakharev, T., Sanjayan, J. and Cheng, Y.B. (2001), "Resistance of alkali-activated slag concrete to carbonation", Cement Concrete Res., 31(9), 1277-1283. https://doi.org/10.1016/S0008-8846(01)00574-9.
  6. Beheshti Aval, S.B., Ketabdari, H. and Asil Gharebaghi, S. (2017), "Estimating shear strength of short rectangular reinforced concrete columns using nonlinear regression and gene expression programming", Struct., 12, 13-23. https://doi.org/10.1016/J.ISTRUC.2017.07.002.
  7. Carevic, V., Ignjatovic, I. and Dragas, J. (2019), "Model for practical carbonation depth prediction for high volume fly ash concrete and recycled aggregate concrete", Constr. Build. Mater., 213, 194-208. https://doi.org/10.1016/j.conbuildmat.2019.03.267.
  8. Castellote, M. and Andrade, C. (2008), "Modelling the carbonation of cementitious matrixes by means of the unreacted-core model, UR-CORE", Cement Concrete Res., 38(12), 1374-1384. https://doi.org/10.1016/J.CEMCONRES.2008.07.004.
  9. Cevik, A. and Sonebi, M. (2008), "Modelling the performance of self-compacting SIFCON of cement slurries using genetic programming technique", Comput. Concrete, 5(5), 475-490. https://doi.org/10.12989/cac.2008.5.5.475.
  10. Dhir, R.K., Munday, J. and Ong, L.T. (1984), "Investigations of the engineering properites of OPC/Pulverised fuel ash concrete: Strength development and maturity", Proc. Inst. Civil Eng., 77(2), 239-254. https://doi.org/10.1680/iicep.1984.1243.
  11. Ferreira, C. (2002), "Gene expression programming in problem solving", Soft Comput. Indus., Springer London, London. https://doi.org/10.1007/978-1-4471-0123-9_54.
  12. Gandomi, A.H., Alavi, A.H., Ting, T.O. and Yang, X.S. (2013), Intelligent Modeling and Prediction of Elastic Modulus of Concrete Strength via Gene Expression Programming, Springer, Berlin, Heidelberg, 564-571.
  13. Gandomi, A.H., Alavi, A.H., Kazemi, S. and Gandomi, M. (2014), "Formulation of shear strength of slender RC beams using gene expression programming, part I: Without shear reinforcement", Autom. Constr., 42, 112-121. https://doi.org/10.1016/J.AUTCON.2014.02.007.
  14. Gepsoft (2014), Gepsoft GeneXproTools - Data Modeling & Analysis Software. https://www.gepsoft.com.
  15. Gholampour, A., Gandomi, A.H. and Ozbakkaloglu, T. (2017), "New formulations for mechanical properties of recycled aggregate concrete using gene expression programming", Constr. Build. Mater., 130, 122-145. https://doi.org/10.1016/J.CONBUILDMAT.2016.10.114.
  16. Gonzalez-Taboada, I., Gonzalez-Fonteboa, B., Martinez-Abella, F. and Perez-Ordonez, J.L. (2016), "Prediction of the mechanical properties of structural recycled concrete using multivariable regression and genetic programming", Constr. Build. Mater., 106, 480-499. https://doi.org/10.1016/J.CONBUILDMAT.2015.12.136.
  17. Hobbs, D.W. (1988), "Carbonation of concrete containing pfa", Mag. Concrete Res., 40(143), 69-78. https://doi.org/10.1680/macr.1988.40.143.69.
  18. Hodhod, O.A., Said, T.E. and Ataya, A.M. (2018), "Prediction of creep in concrete using genetic programming hybridized with ANN", Comput. Concrete, 21(5), 513-523. https://doi.org/10.12989/cac.2018.21.5.513.
  19. Houst, Y.F. (1996) The Role of Moisture in the Carbonation of Cementitious Materials.
  20. Jafari, S. and Mahini, S.S. (2017), "Lightweight concrete design using gene expression programing", Constr. Build. Mater., 139, 93-100. https://doi.org/10.1016/J.CONBUILDMAT.2017.01.120.
  21. Jiang, L., Lin, B. and Cai, Y. (2000), "A model for predicting carbonation of high-volume fly ash concrete", Cement Concrete Res., 30(5), 699-702. https://doi.org/10.1016/S0008-8846(00)00227-1.
  22. Koza, J. (1994), "Genetic programming as a means for programming computers by natural selection", Stat. Comput., 4(2), 87-112. https://doi.org/10.1007/BF00175355.
  23. Kwon, S.J. and Song, H.W. (2010), "Analysis of carbonation behavior in concrete using neural network algorithm and carbonation modeling", Cement Concrete Res., 40(1), 119-127. https://doi.org/10.1016/J.CEMCONRES.2009.08.022.
  24. Lim, J.C., Karakus, M. and Ozbakkaloglu, T. (2016), "Evaluation of ultimate conditions of FRP-confined concrete columns using genetic programming", Comput. Struct., 162, 28-37. https://doi.org/10.1016/J.COMPSTRUC.2015.09.005.
  25. Liu, Q.F., Iqbal, M.F., Yang, J., Lu, X.Y., Zhang, P. and Rauf, M. (2020), "Prediction of chloride diffusivity in concrete using artificial neural network: Modelling and performance evaluation", Constr. Build. Mater., 121082. https://doi.org/10.1016/j.conbuildmat.2020.121082.
  26. Lo, T.Y., Tang, W.C. and Nadeem, A. (2008), "Comparison of carbonation of lightweight concrete with normal weight concrete at similar strength levels", Constr. Build. Mater., 22(8), 1648-1655. https://doi.org/10.1016/J.CONBUILDMAT.2007.06.006.
  27. Lo, Y. and Lee, H.M. (2002), "Curing effects on carbonation of concrete using a phenolphthalein indicator and Fourier-transform infrared spectroscopy", Build. Environ., 37(5), 507-514. https://doi.org/10.1016/S0360-1323(01)00052-X.
  28. Mills, R.H. (1966), "Factors influencing cessation of hydration in water cured cement pastes", Highway Research Board Special Report, 90. https://trid.trb.org/view/101449.
  29. Mousavi, S.M., Aminian, P., Gandomi, A.H., Alavi, A.H. and Bolandi, H. (2012), "A new predictive model for compressive strength of HPC using gene expression programming", Adv. Eng. Softw., 45(1), 105-114. https://doi.org/10.1016/J.ADVENGSOFT.2011.09.014.
  30. Murad, Y., Imam, R., Hajar, H.A., Hammad, A. and Shawash, Z. (2019), "Predictive compressive strength models for green concrete", Int. J. Struct. Integrity, 11(2), 169-184. https://doi.org/10.1108/IJSI-05-2019-0044
  31. Murad, Y., Abdel-Jabar, H., Diab, A. and Hajar, H.A. (2020), "Exterior RC joints subjected to monotonic and cyclic loading", Eng. Comput. (Swansea, Wales), 37(7), 2319-2336. https://doi.org/10.1108/EC-06-2019-0269.
  32. Murad, Y. (2020), "Joint shear strength models for exterior RC beam-column connections exposed to biaxial and uniaxial cyclic loading", J. Build. Eng., 30, 101225. https://doi.org/10.1016/j.jobe.2020.101225.
  33. Murad, Y., Ashteyat, A. and Hunaifat, R. (2019), "Predictive model to the bond strength of FRP-to-concrete under direct pullout using gene expression programming", J. Civil Eng. Manage., 25(8), 773-784. https://doi.org/10.3846/jcem.2019.10798.
  34. Murad, Y.Z., Hunifat, R. and AL-Bodour, W. (2020), "Interior reinforced concrete beam-to-column joints subjected to cyclic loading: Shear strength prediction using gene expression programming", Case Stud. Constr. Mater., 13, e00432. https://doi.org/10.1016/j.cscm.2020.e00432.
  35. Nazari, A. and Pacheco Torgal, F. (2013), "Modeling the compressive strength of geopolymeric binders by gene expression programming-GEP", Exp. Syst. Appl., 40(14), 5427-5438. https://doi.org/10.1016/J.ESWA.2013.04.014.
  36. Obiedat, E. (2011) Prediction of Carbonation Depth in Concrete using Artificial Neural Networks, Jordan University of Science and Technology.
  37. Osborne, G.J. (1999), "Durability of portland blast-furnace slag cement concrete", Cement Concrete Compos., 21(1), 11-21. https://doi.org/10.1016/S0958-9465(98)00032-8.
  38. Ozcan, F. (2012), "Gene expression programming based formulations for splitting tensile strength of concrete", Constr. Build. Mater., 26(1), 404-410. https://doi.org/10.1016/J.CONBUILDMAT.2011.06.039.
  39. Papadakis, V.G., Vayenas, C.G. and Fardis, M.N. (1991), "Fundamental modeling and experimental investigation of concrete carbonation", ACI Mater. J., 88(4), 363-373. https://doi.org/10.14359/1863.
  40. Peter, M.A., Muntean, A., Meier, S.A. and Bohm, M. (2008), "Competition of several carbonation reactions in concrete: A parametric study", Cement Concrete Res., 38(12), 1385-1393. https://doi.org/10.1016/J.CEMCONRES.2008.09.003.
  41. Rahman, M.M., Jumaat, M.Z. and Islam, A.B.M.S. (2017), "Weight minimum design of concrete beam strengthened with glass fiber reinforced polymer bar using genetic algorithm", Comput. Concrete, 19(2), 127-131. https://doi.org/10.12989/cac.2017.19.2.127.
  42. Roy, S.K., Poh, K.B. and Northwood, D.. (1999), "Durability of concrete-accelerated carbonation and weathering studies", Build. Environ., 34(5), 597-606. https://doi.org/10.1016/S0360-1323(98)00042-0.
  43. 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.
  44. Saridemir, M. (2017), "Modelling the flexural strength of mortars containing different mineral admixtures via GEP and RA", Comput. Concrete, 19(6), 717-724. https://doi.org/10.12989/cac.2017.19.6.717.
  45. Shirkhani, A., Davarnia, D. and Azar, B.F. (2019), "Prediction of bond strength between concrete and rebar under corrosion using ANN", Comput. Concrete, 23(4), 273-279. https://doi.org/10.12989/cac.2019.23.4.273.
  46. Soleimani, S., Rajaei, S., Jiao, P., Sabz, A. and Soheilinia, S. (2018), "New prediction models for unconfined compressive strength of geopolymer stabilized soil using multi-gen genetic programming", Measure., 113, 99-107. https://doi.org/10.1016/J.MEASUREMENT.2017.08.043.
  47. 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.
  48. Song, H.W., Kwon, S.J., Byun, K.J. and Park, C.K. (2006), "Predicting carbonation in early-aged cracked concrete", Cement Concrete Res., 36(5), 979-989. https://doi.org/10.1016/J.CEMCONRES.2005.12.019.
  49. Steffens, A., Dinkler, D. and Ahrens, H. (2002), "Modeling carbonation for corrosion risk prediction of concrete structures", Cement Concrete Res., 32(6), 935-941. https://doi.org/10.1016/S0008-8846(02)00728-7.
  50. Taffese, W.Z., Sistonen, E. and Puttonen, J. (2015), "CaPrM: Carbonation prediction model for reinforced concrete using machine learning methods", Constr. Build. Mater., 100, 70-82. https://doi.org/10.1016/j.conbuildmat.2015.09.058.
  51. Wang, X.Y. and Lee, H.S. (2009), "A model for predicting the carbonation depth of concrete containing low-calcium fly ash", Constr. Build. Mater., 23(2), 725-733. https://doi.org/10.1016/J.CONBUILDMAT.2008.02.019.