Computers and Concrete

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Utilization of support vector machine for prediction of fracture parameters of concrete

  • Samui, Pijush (Centre for Disaster Mitigation and Management, VIT University) ;
  • Kim, Dookie (Department of Civil Engineering, Kunsan National University)
  • Received : 2010.07.19
  • Accepted : 2011.05.26
  • Published : 2012.03.25
⟨ Previous Next ⟩

Abstract

This article employs Support Vector Machine (SVM) for determination of fracture parameters critical stress intensity factor ($K^s_{Ic}$) and the critical crack tip opening displacement ($CTOD_c$) of concrete. SVM that is firmly based on the theory of statistical learning theory, uses regression technique by introducing ${\varepsilon}$-insensitive loss function has been adopted. The results are compared with a widely used Artificial Neural Network (ANN) model. Equations have been also developed for prediction of $K^s_{Ic}$ and $CTOD_c$. A sensitivity analysis has been also performed to investigate the importance of the input parameters. The results of this study show that the developed SVM is a robust model for determination of $K^s_{Ic}$ and $CTOD_c$ of concrete.

Keywords

References

  1. Bazant, Z.P. and Kazemi, M.T. (1990), "Determination of fracture energy, process zone length and brittleness number from size effect with application to rock and concrete", Int. J. Fracture, 44(2), 111-31. https://doi.org/10.1007/BF00047063
  2. Bazant, Z.P. and Oh, B.H. (1983), "Crack band theory for fracture concrete", Mater. Struct., 16(93), 155-177.
  3. Bennett, K.P. and Mangasarian, O.L. (1992), "Robust linear programming discrimination of two linearly inseparable sets", Optim. Method. Softw., 1(1), 23-34. https://doi.org/10.1080/10556789208805504
  4. Boser, B.E., Guyon, I.M. and Vapnik, V.N. (1992), "A training algorithm for optimal margin classifiers", In D. Haussler, editor, 5th Annual ACM Workshop on COLT, Pittsburgh, PA, ACM Press, 144-152.
  5. Cortes, C. and Vapnik, V.N. (1995), "Support vector networks machine learning", 20(3), 273-297.
  6. Cristianini, N. and Shawe Taylor, J. (2000), "An introduction to support vector machine", Cambridge University press, London.
  7. "Fuzzy Logic Models", The MIT press, Cambridge, Massachusetts, London, England.
  8. Gualtieri, J.A., Chettri, S.R., Cromp, R..F. and Johnson, L.F. (1999), "Support vector machine classifiers as applied to AVIRIS data", In the Summaries of the Eighth JPL Airbrone Earth Science Workshop.
  9. Hillerborg, A., Modeer, M. and Petersson, P.E. (1976), "Analysis of crack formation and crack growth in concrete by means of fracture mechanics and finite elements", Cement Concrete, 6(6), 773-781. https://doi.org/10.1016/0008-8846(76)90007-7
  10. Hilsdorf, H.H. and Brameshuber, W. (1991), "Code-type formulation of fracture mechanics concepts for concrete", Int. J. Fracture, 51(1), 61-72. https://doi.org/10.1007/BF00020853
  11. Ince, R. (2004), "Prediction of fracture parameters of concrete by Artificial Neural Networks", Eng. Fract. Mech., 71(15), 2143-2159. https://doi.org/10.1016/j.engfracmech.2003.12.004
  12. Jenq, Y.S. and Shah, S.P. (1985), "Two-parameter fracture model for concrete", J. Eng. Mech.-ASCE, 111(10), 1227-1241. https://doi.org/10.1061/(ASCE)0733-9399(1985)111:10(1227)
  13. Kecman, V. (2001), Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy logic Models, The MIT Press, Cambridge, MA, 2001.
  14. Liong, S.Y., Lim, W.H. and Paudyal, G.N. (2000), "River stage forecasting in Bangladesh: neural network approach", J. Comput. Civil Eng., 14(1), 1-8. https://doi.org/10.1061/(ASCE)0887-3801(2000)14:1(1)
  15. Mukherjee, S., Osuna, E. and Girosi, F. (1997), "Nonlinear prediction of chaotic time series using support vector machine", Proc., IEEE Workshop on Neural Networks for Signal Processing 7, Institute of Electrical and Electronics Engineers, New York, 511-519.
  16. Muller, K.R., Smola, A., Ratsch, G., Scholkopf, B., Kohlmorgen, J. and Vapnik, V. (1997), "Predicting time series with support vector machines", Proc., Int. Conf. on Artificial Neural Networks, Springer-Verlag, Berlin.
  17. Nallathambi, P. and Karihaloo, B.L. (1986), "Determination of the specimen size independent fracture toughness of plain concrete", Mag. Concrete Res., 38(135), 67-76. https://doi.org/10.1680/macr.1986.38.135.67
  18. Park, D. and Rilett, L.R. (1999), "Forecasting freeway link travel times with a multi-layer feed forward neural network", Comput.-Aided Civ. Inf., 14(5), 358-367.
  19. Samui, P. (2008), "Support vector machine applied to settlement of shallow foundations on cohesionless soils", Comput. Goetech., 35(3), 419-427. https://doi.org/10.1016/j.compgeo.2007.06.014
  20. Samui, P., Kurup, P.U. and Sitharam, T.G. (2008), "OCR prediction using support vector machine based on piezocone data", J. Geotech. Geoenviron., 134(6), 894-898. https://doi.org/10.1061/(ASCE)1090-0241(2008)134:6(894)
  21. Tang, T., Ouyang, C. and Shah, S.P. (1996), "A simple method for determining material fracture parameters from peak loads", ACI Mater. J., 93(2), 147-157.
  22. Vapnik, V.N. (1995), The nature of statistical learning theory, Springer, New York.

Cited by

  1. Support vector machine for prediction of the compressive strength of no-slump concrete vol.11, pp.4, 2013, https://doi.org/10.12989/cac.2013.11.4.337
  2. Support vector machines in structural engineering: a review vol.21, pp.3, 2015, https://doi.org/10.3846/13923730.2015.1005021
  3. Evaluation of the Productivity of Ready Mixed Concrete Batch Plant Using Artificial Intelligence Techniques vol.901, pp.None, 2012, https://doi.org/10.1088/1757-899x/901/1/012020