Computers and Concrete

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Modeling properties of self-compacting concrete: support vector machines approach

  • Siddique, Rafat (Department of Civil Engineering, Thapar University) ;
  • Aggarwal, Paratibha (Department of Civil Engineering, N.I.T. Kurukshetra) ;
  • Aggarwal, Yogesh (Department of Civil Engineering, N.I.T. Kurukshetra) ;
  • Gupta, S.M. (Department of Civil Engineering, N.I.T. Kurukshetra)
  • Received : 2007.06.26
  • Accepted : 2008.07.29
  • Published : 2008.10.25
⟨ Previous Next ⟩

Abstract

The paper explores the potential of Support Vector Machines (SVM) approach in predicting 28-day compressive strength and slump flow of self-compacting concrete. Total of 80 data collected from the exiting literature were used in present work. To compare the performance of the technique, prediction was also done using a back propagation neural network model. For this data-set, RBF kernel worked well in comparison to polynomial kernel based support vector machines and provide a root mean square error of 4.688 (MPa) (correlation coefficient=0.942) for 28-day compressive strength prediction and a root mean square error of 7.825 cm (correlation coefficient=0.931) for slump flow. Results obtained for RMSE and correlation coefficient suggested a comparable performance by Support Vector Machine approach to neural network approach for both 28-day compressive strength and slump flow prediction.

Keywords

References

  1. Bouzoubaa, N. and Lachemi, M. (2001), "Self-compacting concrete incorporating high volumes of class F fly ash Preliminary results", Cement Concrete Res., 31, 413-420. https://doi.org/10.1016/S0008-8846(00)00504-4
  2. Bui, V.K., Akkaya, Y. and Shah, S.P. (2002), "Rheological model for self-consolidating concrete", ACI Mater. J., 99(6), 549-559.
  3. Chengju, G. (1989), "Maturity of concrete: Method for predicting early stage strength", ACI Mater. J., 86(4), 341-353.
  4. Dias, W.P.S. and Pooliyadda, S.P. (2001), "Neural networks for predicting properties of concretes with Admixtures", Constr. Build. Mater., 15, 371-379. https://doi.org/10.1016/S0950-0618(01)00006-X
  5. Dibike, Y.B., Velickov, S., Solomatine, D.P. and Abbott, M.B. (2001), "Model induction with support vector machines: Introduction and applications", J. Comput. Civ. Eng., ASCE, 15, 208-216. https://doi.org/10.1061/(ASCE)0887-3801(2001)15:3(208)
  6. Ghezal, A. and Khayat, K.H. (2002), "Optimizing self-consolidating concrete with limestone filler by using statistical factorial design methods", ACI Mater. J., 99(3), 264-268.
  7. Hong-Guang, N. and Ji-Zong, W. (2000), "Prediction of compressive strength of concrete by neural networks" Cement Concrete Res., 3(8), 1245-1250.
  8. Kasperkiewicz, J., Rach, J. and Dubrawski, A. (1995), "HPC strength prediction using Artificial neural network", J. Compu. Civ. Engg., 9(4), 279-284. https://doi.org/10.1061/(ASCE)0887-3801(1995)9:4(279)
  9. Kim, J.I., Kim, D. K., Feng, M.Q. and Yazdani, F. (2004), "Application of Neural Networks for Estimation of Concrete Strength", J. Mater. Civ. Eng., 16(3), 257-264. https://doi.org/10.1061/(ASCE)0899-1561(2004)16:3(257)
  10. Ji, T. and Lin, X.J. (2006), "A mortar mix proportion design algorithm based on artificial neural networks". Computers and Concrete, 3(5), 357-373. https://doi.org/10.12989/cac.2006.3.5.357
  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. (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. Lee, J.J., Kim, D.K., Chang, S.K., and Lee, J.H. (2007), "Application of support vector regression for the prediction of concrete strength", Comput. Concrete, 4(4), 299-316. https://doi.org/10.12989/cac.2007.4.4.299
  14. Nagamoto, N. and Ozawa, K. (1997), "Mixture properties of self-compacting, High performance concrete" Third CANMET/ ACI International Conference on Design and Materials and Recent Advances in Concrete Technology, SP-172, V.M.Malthotra, ed., American Concrete Institute, Farmington Hills, Mich., 623-627.
  15. Nehdi, M., Chabib, H.E. and Naggar, M.H.E. (2001), "Predicting performance of self-compacting concrete mixtures using artificial neural networks" ACI Mater. J., 98(5), 394-401.
  16. Oh, J.W., Kim, J.T. and Lee, G.W. (1999), "Application of neural networks for proportioning of concrete mixes", ACI Mater. J., 96(1), 61-67.
  17. Okamura, H. (1997), "Self-compacting concrete-Ferguson Lecture for 1996", Concr. Int., 19(7), 50-54.
  18. Oluokun, F.A., Burdette, E.G. and Deatherage J.H. (1990), "Early-age concrete strength prediction by maturity - Another look", ACI Mater. J., 87(6), 565-572.
  19. Pal, M. and Mather, PM. (2003), "Support vector classifiers for land cover classification", Map India 2003, New Delhi, 28-31 January, www.gisdevelopment.net/technology/rs/ pdf/23.pdf.
  20. Patel, R., Hossain, K.M.A., Shehata, M., Bouzoubaa, N. and Lachemi, M. (2004), "Development of statistical models for mixture design of high-volume fly ash self-consolidation concrete", ACI Mater. J., 101(4), 294-302.
  21. Platt, J.C. (1999), "Fast training of support vector machines using sequential minimal optimization". Advances in Kernels Methods: Support vector machines, Scholkopf, B, Burges, C. and Smola, A. (Eds.), Cambridge, MA: MIT Press.
  22. Popovics, S. (1998), " History of a mathematical model for strength development of Portland cement concrete". ACI Mater. J., 95(5), 593-600.
  23. Ren, L.Q. and Zhao, Z.Y. (2002), "An optimal neural network and concrete strength modeling". J. Adv. Eng. Software, 33, 117-130. https://doi.org/10.1016/S0965-9978(02)00005-4
  24. 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, 1137-1146. https://doi.org/10.1016/S0008-8846(03)00019-X
  25. Smola, A.J. (1996), "Regression estimation with support vector learning machines", Master's Thesis, Technische Universitat Munchen, Germany.
  26. Snell, L.M., Van Roekel, J. and Wallace, N.D. (1989), "Predicting early concrete strength", Concrete Int., 11(12), 43-47.
  27. Sonebi, M. (2004a), "Application of statistical models in proportioning medium strength self-consolidating concrete" ACI Mater. J., 101(5), 339-346.
  28. Sonebi, M. (2004b), "Medium strength self-compacting concrete containing fly ash: Modelling using factorial experimental plans", Cement Concrete Res., 34(7), 1199-1208. https://doi.org/10.1016/j.cemconres.2003.12.022
  29. Witten, I.H. and Frank, E. (1999), Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco.
  30. Vapnik, V.N. (1995), The Nature of Statistical Learning Theory, Springer-Verlag, New York.
  31. Yeh, I-Cheng. (1998a), "Modeling concrete strength using augment-neuron network", J. Mater. Civ. Eng., 10(4), Nov.
  32. Yeh, I-Cheng. (1998b), "Modeling of strength of high-performance concrete using artificial neural networks", Cement Concrete Res., 28(12), 1797-1808. https://doi.org/10.1016/S0008-8846(98)00165-3
  33. Yeh, I-Cheng. (1999), "Design of high-performance concrete mixture using neural networks and nonlinear programming". , J. Comput. Civ. Eng., 13(1), Jan.
  34. Yeh, I-Cheng. (2008), "Prediction of workability of concrete using design of experiments for mixtures". Comput. Concrete, 5(1), 1-20. https://doi.org/10.12989/cac.2008.5.1.001

Cited by

  1. Support vector machines in structural engineering: a review vol.21, pp.3, 2015, https://doi.org/10.3846/13923730.2015.1005021
  2. Establishing a cost-effective sensing system and signal processing method to diagnose preload levels of ball screws vol.28, 2012, https://doi.org/10.1016/j.ymssp.2011.10.004
  3. A thorough study on the effect of red mud, granite, limestone and marble slurry powder on the strengths of steel fibres-reinforced self-consolidation concrete: Experimental and numerical prediction vol.44, pp.None, 2008, https://doi.org/10.1016/j.jobe.2021.103398