DOI QR코드

DOI QR Code

Prediction of compressive strength for HPC mixes containing different blends using ANN

  • Lingam, Allam (Department of Civil Engineering, National Institute of Technology) ;
  • Karthikeyan, J. (Department of Civil Engineering, National Institute of Technology)
  • 투고 : 2012.05.08
  • 심사 : 2013.12.27
  • 발행 : 2014.05.28

초록

This paper is aimed at adapting Artificial Neural Networks (ANN) to predict the compressive strength of High Performance Concrete (HPC) containing binary and quaternary blends. The investigations were done on 23 HPC mixes, and specimens were cast and tested after 7, 28 and 56 days curing. The obtained experimental datas of 7, 28 and 56 days are trained using ANN which consists of eight input parameters like cement, metakaolin, blast furnace slag and fly ash, fine aggregate, coarse aggregate, superplasticizer and water binder ratio. The corresponding output parameters are 7, 28 and 56 days compressive strengths. The predicted values obtained using ANN show a good correlation between the Experimental data. The performance of the 8-9-3-3 architecture was better than other architectures. It concluded that ANN tool is convenient and time saving for predicting compressive strength at different ages.

키워드

참고문헌

  1. ACI committee 211 (2008), Guide for selecting proportions for High-Strength concrete using Portland cement and other cementitious materials, ACI 211. 4R-08, December.
  2. Adhikary, B.B. and Mutsuyoshi, H. (2006), "Prediction of shear strength of steel fiber RC beams using neural networks", Constr. Build. Mate., 20 (9), 801-811. https://doi.org/10.1016/j.conbuildmat.2005.01.047
  3. Bai, J., Sabir, B.B., Wild, S. and Kinuthia, J.M. (2000), "Strength development in concrete incorporating PFA and metakaolin", Magazine of Concrete Research, 52 (3), 153-162. https://doi.org/10.1680/macr.2000.52.3.153
  4. Bilim, C., Cengiz, D., Atis, H.T. and Karahan, O. (2009), " Predicting the Compressive strength of ground granulated blast furnace slag concrete using artificial neural network", Adv. Eng. Softw., 40, 334-340. https://doi.org/10.1016/j.advengsoft.2008.05.005
  5. Curciol, F. and Deangelis, B.A. (1998), "Dilatant behavior of superplasticized cement pastes containing metakaolin", Cement Concrete Res., 28 (5), 629-634. https://doi.org/10.1016/S0008-8846(98)00046-5
  6. Demir, F. (2008), "Prediction of elastic modulus of normal and high strength concrete by artificial neural networks", Constr. Build. Mater, 22 (7), 1428-1435. https://doi.org/10.1016/j.conbuildmat.2007.04.004
  7. Eldin, N.N. and Senouci, A.B. (1994), "Measurement and prediction of the strength of rubberized concrete", J. Cement Concrete Compos., 16, 287-298. https://doi.org/10.1016/0958-9465(94)90041-8
  8. Hanbay, D., Turkoglu, I. and Demir, Y. (2008), "An expert system based on wavelet decomposition and neural network for modeling chua's circuit", Exp. Syst. Appl., 34 (4), 2278-2283. https://doi.org/10.1016/j.eswa.2007.03.002
  9. Hanbay. D., Turkoglu, I. and Demir, Y. (2008), "Prediction of wastewater treatment plant performance based on wavelet packet decomposition and neural networks", Exp. Syst Appl., 34 (2), 1038-1043. https://doi.org/10.1016/j.eswa.2006.10.030
  10. Haykin, S. (1994), Neural Networks, A Comprehensive Foundation, College Publishing Comp. Inc., 1994.
  11. Hola, J. and Schabowicz, K. (2004), "New technique of nondestructive assessment of concrete strength using artificial intelligence", J. NDT E Int.
  12. Kewalramani, A.M. and Gupta, R. (2006), "Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks", Auto Constr., 15 (15), 374-379. https://doi.org/10.1016/j.autcon.2005.07.003
  13. Lai, S. and Serra, M. (1997), "Concrete strength prediction by means of neural network", J. Construct. Build. Mater., 11(2), 93-98. https://doi.org/10.1016/S0950-0618(97)00007-X
  14. Langley, W.S., Carette, G.G. and Malhotra, V.M. (1989), "Structural concrete incorporating high volumes of ASTM class F fly ash", ACI Mater. J., 86, 507- 514.
  15. Lynsdale, C.J. and Khan M.I. (2000), "Chloride and oxygen permeability of concrete incorporating fly ash and silica fume in ternary systems", in V.M. Malhotra (Ed.), Proceedings of the 5th CANMET/ACI International Conference on Durability of Concrete, Barcelona, Spain, 2, 739-753, SP-192.
  16. Malhotra V.M. (2006), "Reducing CO2 emissions", Concr. Int., 28 (9), 42-45.
  17. Malhotra, V.M. (1990), "Durability of concrete incorporating high-volume of low-calcium (ASTM class F) flyash", Cement Concrete Compos., 12 (4), 271-277. https://doi.org/10.1016/0958-9465(90)90006-J
  18. Mansour, M.Y., Dicleli, M., Lee. J.Y. and Zhang, J. (2004), "Predicting the shear strength of reinforced concrete beams using artificial neural network", Eng. Struct., 26 (6), 781-799. https://doi.org/10.1016/j.engstruct.2004.01.011
  19. Mehta, P.K. (2002), "Greening of the concrete industry for sustainable development", Concr. Int., 24 (7), 22-28.
  20. Mehta, P.K. and Monteiro, J.P.M. (2006), Concrete: Microstructure, Properties and Materials, McGraw- Hill, 3rd ed, New York.
  21. Menendez, G., Bonavetti, V. and Irassar, E.F. (2003), "Strength development of ternary blended cement with limestone filler and blast-furnace slag", Cem. Concr. Compos., 25 (1), 61-67. https://doi.org/10.1016/S0958-9465(01)00056-7
  22. Meyer, C. (2009), "The greening of the concrete industry", Cement Concrete Composites, 31 (8), 601-605. https://doi.org/10.1016/j.cemconcomp.2008.12.010
  23. Mindess, S., Young, J. and Darwin, D. (2003), Concrete, Prentice- Hall, 2nd ed, Upper Saddle River.
  24. Khan, M.I. (2011), "Predicting properties of High Performance Concrete containing composite cementitious materials using Artificial Neural networks", Automat. Constr., 22, 516-524.
  25. Nirma Farzadnia (2011), "Incorporation of mineral admixtures in sustainable high performance concrete", Int. J. Sustainable Construct. Eng. Tech., 2(1).
  26. Noorzaei, J., Hakim, S.J.S., Jaafar, M.S. and Thanoon, W.A.M. (2007), "Development of artificial neural networks for predicting concrete compressive strength", Int. J. Eng. Tech., 4, 141-153.
  27. Oluokun, F.A. (1994), ACI Mater. J., 91, 362.
  28. Parande, A.K. (2013), "Role of ingredients for high strength and high performance concrete - A review", Adv. Concrete Construct., 1(2), 151-162. https://doi.org/10.12989/acc.2013.01.2.151
  29. Popovics, S. (1990), ACI Mater. J., 87, 517.
  30. Rafiq, M.Y., Bugmann, G. and Easter brook, D.J. (2001), "Neural network design for engineering Applications", Comput. Struct., 79 (17), 1541-1552. https://doi.org/10.1016/S0045-7949(01)00039-6
  31. Ramezanianpour, A.A. and Bahrami Jovein, H. (2011), "Influence of metakaolin as supplementary cementing material on strength and durability of concretes", Construct. Build. Mater., 30, 470-479.
  32. Saridemir, M., Topcu, I.B., Ozcan, F. and Severcan, M.H. (2009), "Prediction of long term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic", Construct. Build. Mater., 23, 1279-1286. https://doi.org/10.1016/j.conbuildmat.2008.07.021
  33. Song, H.W. and Saraswathy, V. (2006), "Studies on the corrosion resistance of reinforced steel in concrete with ground granulated blast-furnace slag - an overview", J Hazard Mater, 138 (2), 226-233. https://doi.org/10.1016/j.jhazmat.2006.07.022
  34. Sun, W., Zhang, Y.S., Liu, S. and Zhang, Y. (2004), "The influence of mineral admixtures on resistance to corrosion of steel bars in green High-performance concrete", Cement Concrete Res., 34, 1781-1785. https://doi.org/10.1016/j.cemconres.2004.01.008
  35. Yeh, I.C. (1998), "Modeling of strength of high-performance concrete using artificial neural networks", J. Cement Concrete Res., 28(12), 1797-1808. https://doi.org/10.1016/S0008-8846(98)00165-3

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