Computers and Concrete

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Prediction of compressive strength of concrete using neural networks

  • Al-Salloum, Yousef A. (Specialty Units for Safety and Preservation of Structures, Department of Civil Engineering, King Saud University) ;
  • Shah, Abid A. (Specialty Units for Safety and Preservation of Structures, Department of Civil Engineering, King Saud University) ;
  • Abbas, H. (Specialty Units for Safety and Preservation of Structures, Department of Civil Engineering, King Saud University) ;
  • Alsayed, Saleh H. (Specialty Units for Safety and Preservation of Structures, Department of Civil Engineering, King Saud University) ;
  • Almusallam, Tarek H. (Specialty Units for Safety and Preservation of Structures, Department of Civil Engineering, King Saud University) ;
  • Al-Haddad, M.S. (Specialty Units for Safety and Preservation of Structures, Department of Civil Engineering, King Saud University)
  • Received : 2010.08.03
  • Accepted : 2012.03.07
  • Published : 2012.08.25
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Abstract

This research deals with the prediction of compressive strength of normal and high strength concrete using neural networks. The compressive strength was modeled as a function of eight variables: quantities of cement, fine aggregate, coarse aggregate, micro-silica, water and super-plasticizer, maximum size of coarse aggregate, fineness modulus of fine aggregate. Two networks, one using raw variables and another using grouped dimensionless variables were constructed, trained and tested using available experimental data, covering a large range of concrete compressive strengths. The neural network models were compared with regression models. The neural networks based model gave high prediction accuracy and the results demonstrated that the use of neural networks in assessing compressive strength of concrete is both practical and beneficial. The performance of model using the grouped dimensionless variables is better than the prediction using raw variables.

Keywords

References

  1. Adhikary, B.B. and Mutsuyoshi, H. (2006), "Prediction of shear strength of steel fiber RC beams using neural networks", Constr. Build Mater., 20(9), 801-811. https://doi.org/10.1016/j.conbuildmat.2005.01.047
  2. Akkurt, S., Ozdemir, S., Tayfur, G. and Akyol, B. (2003), "The use of GA-ANNs in the modelling of compressive strength of cement mortar", Cement. Concrete Res., 33(7), 973-979. https://doi.org/10.1016/S0008-8846(03)00006-1
  3. Baykaso lu, A., Dereli, T. and Tan s, S. (2004), "Prediction of cement strength using soft computing techniques", Cement. Concrete Res., 34(11), 2083-2090. https://doi.org/10.1016/j.cemconres.2004.03.028
  4. Bilgehan, M. and Turgut, P. (2010), "The use of neural networks in concrete compressive strength estimation", Comput. Concrete, 7(3), 271-283. https://doi.org/10.12989/cac.2010.7.3.271
  5. Bilim, C., Atis, C.D., Tanyildizi, H. and Karahan, O. (2009), "Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network", Adv. Eng. Softw., 40(5), 334-340. https://doi.org/10.1016/j.advengsoft.2008.05.005
  6. Castro, P.F. (1987), "Concrete strength-comparison between non-destructive tests", Proc of 4th Int Conf on Durability of Building Materials and Components, Singapore, 885-890.
  7. Dias, W.P.S. and Pooliyadda, S.P. (2001), "Neural networks for predicting properties of concretes with admixtures", Constr. Build. Mater., 15(7), 371-379. https://doi.org/10.1016/S0950-0618(01)00006-X
  8. Hola, J. and Schabowicz, K. (2005), "Application of artificial neural networks to determine concrete compressive strength based on non-destructive tests", J. Civil Eng. Manag., 11(1), 23-32.
  9. Kaveh, A. and Khalegi, A. (1998), "Prediction of strength for concrete specimens using artificial neural networks", Adv. Civil Eng. Comput. Technol., Ed. B.H.V. Topping, Saxe-Coburg Publications, Edinburgh, 165-171.
  10. Kheder, G.F., Al-Gabban, A.M. and Abid, S.M. (2003), "Mathematical model for the prediction of cement compressive strength at the ages of 7 and 28 days within 24 hours", Mater. Struct., 36(10), 693-701. https://doi.org/10.1007/BF02479504
  11. Lai, S. and Serra, M. (1997), "Concrete strength prediction by means of neural network", Constr. Build. Mater., 11(2), 93-98. https://doi.org/10.1016/S0950-0618(97)00007-X
  12. Lee, S.C. (2003), "Prediction of concrete strength using artificial neural networks", Eng. Struct., 25(7), 849-857. https://doi.org/10.1016/S0141-0296(03)00004-X
  13. Lyse, I. (1932), Test on consistence and strength of concrete having constant water content, ASTM Proc, 32(1), 629-636.
  14. Mansour, M.Y., Dicleli, M., Lee, J.Y. and Zhang, J. (2004), "Predicting the shear strength of reinforced concrete beams using artificial neural networks", Eng. Struct., 26(6), 781-799. https://doi.org/10.1016/j.engstruct.2004.01.011
  15. Marquardt, D. (1963), "An algorithm for least-squares estimation of nonlinear parameters", SIAM J. Appl. Math., 11(2), 431-441. https://doi.org/10.1137/0111030
  16. Naderpour, H., Kheyroddin, A. and Amiri, G.G. (2010), "Prediction of FRP-confined compressive strength of concrete using artificial neural networks", Compos. Struct., 92(12), 2817-2829. https://doi.org/10.1016/j.compstruct.2010.04.008
  17. Neville, A.M. (1995), Properties of concrete, Prentice Hall.
  18. Ozcan, F., Atis, C.D., Karahan, O., Uncuo lu, E. and Tanyildiz, H. (2009), "Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete", Adv. Eng. Softw., 40(9), 856-863. https://doi.org/10.1016/j.advengsoft.2009.01.005
  19. Oztas, A., Pala, M., Ozbay, E., Kanca, E., Caglar, N. and Bhatti, M.A. (2006), "Predicting the compressive strength and slump of high strength concrete using neural network", Constr. Build. Mater., 20(9), 769-775. https://doi.org/10.1016/j.conbuildmat.2005.01.054
  20. Pala, M., Ozbay, O., Oztas, A. and Yuce, M.I. (2005), "Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks", Constr. Build. Mater., 21(2), 384-394.
  21. Park, B.J., Pedrycz, W. and Oh, S.K. (2002), "Fuzzy polynomial neural networks: hybrid architectures of fuzzy modeling", IEEE T. Fuzzy. Syst., 10(5), 607-621. https://doi.org/10.1109/TFUZZ.2002.803495
  22. Popovics, S. and Ujhelyi, J. (2008), "Contribution to the concrete strength versus water-cement ratio relationship", J. Mater. Civil. Eng., 20(7), 459-463. https://doi.org/10.1061/(ASCE)0899-1561(2008)20:7(459)
  23. Ramezanianpour, A.A., Sobhani, M. and Sobhani, J. (2004), "Application of network based neuro-fuzzy system for prediction of the strength of high strength concrete", Am. J. Sci. Technol., 5(59), 78-93.
  24. Rasa, E., Ketabchi, H. and Afshar, M.H. (2009), "Predicting density and compressive strength of concrete cement paste containing silica fume using artificial neural networks", Sci. Iran., 16(1), 32-42.
  25. Sarydemir, M. (2009), "Predicting the compressive strength of mortars containing metakaolin by artificial neural networks and fuzzy logic", Adv. Eng. Softw., 40(9), 920-927. https://doi.org/10.1016/j.advengsoft.2008.12.008
  26. Sebastia, M., Olmo, I.F. and Irabien, A. (2003), "Neural network prediction of unconfined compressive strength of coal fly ash-cement mixtures", Cement. Concrete Res., 33(8), 1137-1146. https://doi.org/10.1016/S0008-8846(03)00019-X
  27. Sobhani, J., Najimi, M., Pourkhorshidi, A.R. and Parhizkar, T. (2010), "Prediction of the compression strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models", Constr. Build. Mater., 24(5), 709-718. https://doi.org/10.1016/j.conbuildmat.2009.10.037
  28. Tang, C.W., Lin, Y. and Kuo, S.F. (2007), "Investigation on correlation between pulse velocity and compressive strength of concrete using ANNs", Comput. Concrete, 4(6), 437-456. https://doi.org/10.12989/cac.2007.4.6.437
  29. Topcu, I.B. and Saridemir, M. (2007), "Prediction of properties of waste AAC aggregate concrete using artificial neural network", Comput. Mater. Sci., 41(1), 117-125. https://doi.org/10.1016/j.commatsci.2007.03.010
  30. Topcu, I.B. and Saridemir, M. (2008a), "Prediction of compressive strength of concrete containing fly ash using artificial neural network and fuzzy logic", Comput. Mater. Sci., 41(3), 305-311. https://doi.org/10.1016/j.commatsci.2007.04.009
  31. Topcu, I.B. and Saridemir, M. (2008b), "Prediction of rubberized concrete properties using artificial neural network and fuzzy logic", Constr. Build. Mater., 22(4), 532-540. https://doi.org/10.1016/j.conbuildmat.2006.11.007
  32. Wang, J.Z., Ni, H.G. and He, J.Y. (1999a), "The application of automatic acquisition of knowledge to mix design of concrete", Cement Concrete Res., 29(12), 1875-1880. https://doi.org/10.1016/S0008-8846(99)00152-0
  33. Wang, J.Z., Wen, X.M., Huang, Y.X. and Jiang, K.C. (1999b), "Dynamics and strength analysis of a MK-II screen", J. China Coal Soc., 24(2), 184-188.
  34. Waszczyszyn, Z. and Ziemianski, L. (2001), "Neural networks in mechanics of structures and materials-new results and prospects of applications", Comput. Struct., 79(22-25), 2261-2276. https://doi.org/10.1016/S0045-7949(01)00083-9
  35. Yeh, I. (1999), "Design of high-performance concrete mixture using neural networks and nonlinear programming", J Comp. Civil Eng., 13(1), 36-42. https://doi.org/10.1061/(ASCE)0887-3801(1999)13:1(36)
  36. Zarandi, M.H.F., Turksen, I.B., Sobhani, J. and Ramezanianpour, A.A. (2008), "Fuzzy polynomial neural networks for approximation of the compressive strength of concrete", Appl. Soft. Comput., 8(1), 488-498. https://doi.org/10.1016/j.asoc.2007.02.010

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