Evaluation of the effect of aggregate on concrete permeability using grey correlation analysis and ANN



Kong, Lijuan;Chen, Xiaoyu;Du, Yuanbo

  • 투고 : 2015.08.01
  • 심사 : 2016.02.04
  • 발행 : 2016.05.25


In this study, the influence of coarse aggregate size and type on chloride penetration of concrete was investigated, and the grey correlation analysis was applied to find the key influencing factor. Furthermore, the proposed 6-10-1 artificial neural network (ANN) model was constructed, and performed under the MATLAB program. Training, testing and validation of the model stages were performed using 81 experiment data sets. The results show that the aggregate type has less effect on the concrete permeability, compared with the size effect. For concrete with a lower w/b, the coarse aggregate with a larger particle size should be chose, however, for concrete with a higher w/c, the aggregate with a grading of 5-20 mm is preferred, too large or too small aggregates are adverse to concrete chloride diffusivity. A new idea for the optimum selection of aggregate to prepare concrete with a low penetration is provided. Moreover, the ANN model predicted values are compared with actual test results, and the average relative error of prediction is found to be 5.62%. ANN procedure provides guidelines to select appropriate coarse aggregate for required chloride penetration of concrete and will reduce number of trial and error, save cost and time.


coarse aggregate;permeability;concrete;grey correlation analysis;artificial neural network


  1. Bal, L. and Buyle-Bodin, F. (2010), "Artificial neural network for predicting drying shrinkage of concrete", Constr. Build. Mater., 38(1), 248-254.
  2. Chindaprasirt, P., Chotithanorm, C., Cao, H.T. and Sirivivatnanon, V. (2007), "Influence of fly ash fineness on the chloride penetration of concrete", Constr. Build. Mater., 21(2), 356-361. https://doi.org/10.1016/j.conbuildmat.2005.08.010
  3. Delagrave, A., Bigas, J.P. and Olivier, J.P. (1997), "Influence of the interfacial zone on the chloride diffusivity of mortars", Adv. Cem. Based Mater., 5(3), 86-92. https://doi.org/10.1016/S1065-7355(96)00008-9
  4. Deng, J.L. (1985), "Grey control system", Huazhong University of Technology Press, Wuhan.
  5. Elsharief, A., Cohen, M.D. and Olek, J. (2003), "Influence of aggregate size, water cement ratio and age on the microstructure of the interfacial zone", Cement Concrete Res., 33(11), 1837-1849. https://doi.org/10.1016/S0008-8846(03)00205-9
  6. Erdem, S., Dawson, A.R. and Thom, N.H. (2012), "Influence of the micro-and nanoscale local mechanical properties of interfacial transition zone on impact behavior of concrete made with different aggregates", Cement Concrete Res., 42(2), 447-458. https://doi.org/10.1016/j.cemconres.2011.11.015
  7. Ji, T., Lin, T.W. and Lin, X.J. (2006), "A concrete mix proportion design algorithm based on artificial neural networks", Cement Concrete Res., 36(7), 1399-1408. https://doi.org/10.1016/j.cemconres.2006.01.009
  8. Khan, M.L. (2012), "Mix proportions for HPC incorporating multi-cementitious composites using artificial neural networks", Constr. Build. Mater., 28(1), 14-20. https://doi.org/10.1016/j.conbuildmat.2011.08.021
  9. Li, C.L., Lu, X.Y. and Zhang, H.X. (1998), "Rapid test method for determining chloride diffusivities in cementitious materials", Ind. Constr., 28(6), 41-43.
  10. Monteiro, P.J.M. and Metha P.K. (1986), "Interaction between carbonate rock and cement paste", Cement Concrete Res., 16(2), 127-134. https://doi.org/10.1016/0008-8846(86)90128-6
  11. Parka, K.B., Noguchi, T. and Plawsky, J. (2005), "Modelling of hydration reactions using neural networks to predict the average properties of cement paste", Cement Concrete Res., 35(9), 1676-1684. https://doi.org/10.1016/j.cemconres.2004.08.004
  12. Pereira, C.G., Gomes, J.C. and Oliveira, L.P. (2009), "Influence of natural coarse aggregate size, mineralogy and water content on the permeability of structural concrete", Constr. Build. Mater., 23(2), 602-608. https://doi.org/10.1016/j.conbuildmat.2008.04.009
  13. Sun, G.W., Sun, W. and Zhang, Y.S. (2012), "Numerical calculation and influencing factors of the volume fraction of interfacial transition zone in concrete", Sci. China Technol. Sci., 55(6), 1515-1522. https://doi.org/10.1007/s11431-011-4737-x
  14. Tasong, W.A., Cripps, J.C. and Lynsdale C.J. (1998), "Aggregate-cement chemical interaction", Cement Concrete Res., 28(7), 1037-1048. https://doi.org/10.1016/S0008-8846(98)00067-2
  15. Torgal, F.P. and Gomes, J.C. (2006), "Influence of physical and geometrical properties of granite and limestone aggregate on the durability of a C20/25 strength class concrete", Constr. Build. Mater., 20(10), 1079-1088. https://doi.org/10.1016/j.conbuildmat.2005.01.063
  16. Wei, J.C., Zhou, L., Wang, F. and Wu, D.S. (2015), "Work safety on evaluation in Mainland China using grey theory", Appl. Math. Model., 39(2), 924-933. https://doi.org/10.1016/j.apm.2014.06.017
  17. Wongkeo, W., Thongsanitgarn, P., Ngamjarurojana, A. and Chaipanich, A. (2014), "Compressive strength and chloride resistance of self-compacting concrete containing high level fly ash and silica fume", Mater. Des., 64(2), 261-269. https://doi.org/10.1016/j.matdes.2014.07.042

피인용 문헌

  1. 1. Multiscale analysis of the correlation of processing parameters on viscidity of composites fabricated by automated fiber placement vol.4, pp.10, 2017, doi:10.12989/cac.2016.17.5.613


연구 과제 주관 기관 : Natural Science Foundation of China, Natural Science Foundation of Hebei Province of China