DOI QR코드

DOI QR Code

Compressive strength prediction of CFRP confined concrete using data mining techniques

  • Camoes, Aires (CTAC, Department of Civil Engineering, University of Minho) ;
  • Martins, Francisco F. (ISISE, Department of Civil Engineering, University of Minho)
  • 투고 : 2016.07.08
  • 심사 : 2016.12.15
  • 발행 : 2017.03.25

초록

During the last two decades, CFRP have been extensively used for repair and rehabilitation of existing structures as well as in new construction applications. For rehabilitation purposes CFRP are currently used to increase the load and the energy absorption capacities and also the shear strength of concrete columns. Thus, the effect of CFRP confinement on the strength and deformation capacity of concrete columns has been extensively studied. However, the majority of such studies consider empirical relationships based on correlation analysis due to the fact that until today there is no general law describing such a hugely complex phenomenon. Moreover, these studies have been focused on the performance of circular cross section columns and the data available for square or rectangular cross sections are still scarce. Therefore, the existing relationships may not be sufficiently accurate to provide satisfactory results. That is why intelligent models with the ability to learn from examples can and must be tested, trying to evaluate their accuracy for composite compressive strength prediction. In this study the forecasting of wrapped CFRP confined concrete strength was carried out using different Data Mining techniques to predict CFRP confined concrete compressive strength taking into account the specimens' cross section: circular or rectangular. Based on the results obtained, CFRP confined concrete compressive strength can be accurately predicted for circular cross sections using SVM with five and six input parameters without spending too much time. The results for rectangular sections were not as good as those obtained for circular sections. It seems that the prediction can only be obtained with reasonable accuracy for certain values of the lateral confinement coefficient due to less efficiency of lateral confinement for rectangular cross sections.

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과제정보

연구 과제 주관 기관 : FCT-Foundation

참고문헌

  1. Aleksander, I. and Morton, H. (1990), An Introduction to Neural Computing, Chapman & Hall.
  2. Ben-Hur, A. and Weston, J. (2010), A User's Guide to Support Vector Machines, Humana Press, New York, U.S.A.
  3. Benzaid, R. and Mesbah, H.A. (2013), "Circular and square concrete columns externally confined by CFRP composite: Experimental investigation and effective strength models", InTech, 167-201.
  4. Berk, R.A. (2008), Statistical Learning from a Regression Perspective, Springer-Verlag, New York, U.S.A.
  5. Berthet, J.F., Ferrier, E. and Hamelin, P. (2005), "Compressive behaviour of concrete externally confined by composite jackets", Constr. Build. Mater., 19(3), 223-232. https://doi.org/10.1016/j.conbuildmat.2004.05.012
  6. Breiman, L., Friedman, J.H., Olshen, R.A. and Stone, C.J. (1984), Classification and Regression Trees, Chapman & Hall/CRC.
  7. Cevik, A. (2011), "Modeling strength enhancement of FRP confined concrete cylinders using soft computing", Exp. Syst. Appl. 38(5), 5662-5673. https://doi.org/10.1016/j.eswa.2010.10.069
  8. Cevik, A. and Cabalar, A.F. (2008), "A genetic-programming-based formulation for the strength of fiber-reinforced-polymer-confined concrete cylinders", J. Appl. Poly. Sci., 110(5), 3087-3095. https://doi.org/10.1002/app.28839
  9. Cevik, A. and Guzelbey, I.H. (2008), "Neural network modeling of strength enhancement for CFRP confined concrete cylinders", Build. Environ., 43(5), 751-763. https://doi.org/10.1016/j.buildenv.2007.01.036
  10. Cevik, A., Gogus, M.T., Guzelbey, I.H. and Filiz, H. (2010), "Soft computing based formulation for strength enhancement of CFRP confined concrete cylinders", Adv. Eng. Soft., 41(4), 527-536. https://doi.org/10.1016/j.advengsoft.2009.10.015
  11. Cherkassy, V. and Ma, Y. (2004), "Practical selection of SVM parameters and noise estimation for SVM regression", Neur. Net., 17(1), 113-126. https://doi.org/10.1016/S0893-6080(03)00169-2
  12. Coimbra, R., Rodriguez-Galiano, V., Oloriz, F. and Chica-Olmo, M. (2014), "Regression trees for modelling geomechanical data-an application to late jurassic carbonates (ammonitico rosso)", Comput. Geosci., 73, 198-207. https://doi.org/10.1016/j.cageo.2014.09.007
  13. Cortes, C. and Vapnik, V. (1995), "Support vector networks", Mach. Learn., 20(3), 273-297. https://doi.org/10.1007/BF00994018
  14. Cortez, P. (2010), "Data mining with neural networks and support vector machines using the r/rminer tool", Proceedings of the 10th Industrial Conference on Data Mining, Advances in Data Mining, Applications and theoretical aspects, Berlin, Germany.
  15. Cover, T.M. (1968), "Estimation by the nearest neighbor rule", IEEE Trans. Informat. Theor., 14(1), 50-55. https://doi.org/10.1109/TIT.1968.1054098
  16. Cover, T.M. and Hart, P.E. (1967), "Nearest neighbor pattern classification", IEEE Trans. Informat. Theor., 13(1), 21-27. https://doi.org/10.1109/TIT.1967.1053964
  17. Cristianini, N. and Shawe-Taylor, J. (2000), An Introduction to Support Vector Machine, University Press, London, U.K.
  18. Czajkowski, M. and Kretowski, M. (2016), "The role of decision trees representation in regression problems-an evolutionary perspective", Appl. Soft Comput., 48, 458-475. https://doi.org/10.1016/j.asoc.2016.07.007
  19. Deniaud, C. and Neale, K.W. (2006), "An assessment of constitutive models for concrete columns confined with fiber composite sheets", Compos. Struct., 73(3), 318-330. https://doi.org/10.1016/j.compstruct.2005.02.003
  20. Dibike, Y.B., Velickov, S., Solomatine, D.P. and Abbott, M.B. (2001), "Model introduction with support vector machines; introduction and applications", J. Comput. Civil Eng., 15(3), 208-216. https://doi.org/10.1061/(ASCE)0887-3801(2001)15:3(208)
  21. Dong, C.X., Kwana, A.K.H. and Hob, J.C.M. (2015), "Effects of confining stiffness and rupture strain on performance of FRP confined concrete", Eng. Struct., 97, 1-14. https://doi.org/10.1016/j.engstruct.2015.03.037
  22. Doran, B., Yetilmezsoy, K. and Murtazaoglu, S. (2015), "Application of fuzzy logic approach in predicting the lateral confinement coefficient for RC columns wrapped with CFRP", Eng. Struct., 88, 74-91. https://doi.org/10.1016/j.engstruct.2015.01.039
  23. Downing, K.L. (2015), Intelligence Emerging: Adaptative and Search in Evolving Neural System, MIT Press, U.S.A.
  24. Efron, B. and Tibshirani, R. (1993), An Introduction to the Bootstrap, Chapman & Hall.
  25. Fayyad, U., Piatesky-Shapiro, G. and Smyth, P. (1996), From Data Mining to Knowledge Discovery: An Overview, IAAAI Press/The MIT Press, Cambridge MA, 471-493.
  26. Gandomi, M., Alavi, A.H. and Sahab, M.G. (2010), "New formulation for compressive strength of CFRP confined concrete cylinders using linear genetic programming", Mater. Struct., 43(7), 963-983. https://doi.org/10.1617/s11527-009-9559-y
  27. Green, M.F., Bisby, L.A., Fam, A.Z. and Kodur, V.K.R. (2006), "FRP confined concrete columns: Behaviour under extreme conditions", Cement Concrete Compos., 28(10), 928-937. https://doi.org/10.1016/j.cemconcomp.2006.07.008
  28. Gupta, S.M. (2007), "Support vector machines based modelling of concrete strength", World Acad. Sci. Eng. Technol., 36, 305-311.
  29. Harajli, M.H., Hantouche, E. and Soudki, K. (2006), "Stress-strain model for fiber-reinforced polymer jacketed concrete columns", ACI Struct. J., 105(5), 672-682.
  30. Haykin, S. (1999), Neural Networks-A Comprehensive Foundation, 2nd Edition, Prentice-Hall, New Jersey, U.S.A.
  31. Hechenbichler, K. and Schliep, K. (2004), "Weighted k-nearest-neighbor techniques and ordinal classification", Ph.D. Dissertation, Ludwig-Maximilians University Munich, Germany.
  32. Hollaway, L.C. (2004), Advanced Polymer Composites for Structural Applications in Construction: ACIC, Woodhead Publishing, U.K.
  33. Ilonen, J., Kamarainen, J.K. and Lampinen, J. (2003), "Differential evolution training algorithm for feed-forward neural network", Neur. Proc. Lett., 17(1), 93-105. https://doi.org/10.1023/A:1022995128597
  34. Jalal, M., Ramezanianpour, A.A., Pouladkhan, A.R. and Tedro, P. (2013), "Application of genetic programming (GP) and ANFIS for strength enhancement modeling of CFRP-retrofitted concrete cylinders", Neur. Comput. Appl., 23(2), 455-470. https://doi.org/10.1007/s00521-012-0941-2
  35. Kewley, R., Embrechts, M. and Brenemam, C. (2000), "Data strip mining for the virtual design of pharmaceuticals with neural networks", IEEE Trans. Neur. Net., 11(3), 668-679. https://doi.org/10.1109/72.846738
  36. Kim, J.I. and Kim, D.K. (2002), "Application of neural networks for estimation of concrete strength", KSCE J. Civil Eng., 6(4), 429-438. https://doi.org/10.1007/BF02841997
  37. Kumutha, R., Vaidyanathan, R. and Palanichamy, M.S. (2007), "Behaviour of reinforced concrete rectangular columns strengthened using GFRP", Cement Concrete Compos., 29(8), 609-615. https://doi.org/10.1016/j.cemconcomp.2007.03.009
  38. 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
  39. Lam, L., Teng, J.G., Cheng, C.H. and Xiao, Y. (2006), "FRP-confined concrete under axial cyclic compression", Cement Concrete Res., 28(10), 949-958. https://doi.org/10.1016/j.cemconcomp.2006.07.007
  40. Lee, C. and Hegemier, G.A. (2009), "Model of FRP-confined concrete cylinders in axial compression", J. Compos. Constr., 13(5), 442-454. https://doi.org/10.1061/(ASCE)CC.1943-5614.0000029
  41. Leung, C.K.Y., Ng, M.Y.M. and Luk, H.C.Y. (2006), "Empirical approach for determining ultimate FRP strain in FRP-strengthened concrete beams", J. Compos. Constr., 10(2), 125-138. https://doi.org/10.1061/(ASCE)1090-0268(2006)10:2(125)
  42. Liang, Y., Xu, Q.S., Li, H.D. and Cao, D.S. (2011), Support Vector Machines and Their Application in Chemistry and Biotechnology, Taylor & Francis CRC Press.
  43. Martins, F.F. and Camoes, A. (2013), "Prediction of compressive strength of concrete containing fly ash using data mining techniques", Cement WapnoBeton, XVIII/LXXX(1), 39-51.
  44. Martins, F.F. and Miranda, T.F.S. (2012), "Estimation of the rock deformation modulus and RMR based on data mining techniques", Geotech. Geol. Eng., 30(4), 787-801. https://doi.org/10.1007/s10706-012-9498-1
  45. Matthys, S., Toutanji, H., Audenaert, K. and Taerwe, L. (2005), "Axial load behavior of largescale columns confined with fiber-reinforced polymer composites", ACI Struct. J., 102(2), 258-267.
  46. Mirmiran, A. and Shahawy, M. (1997), "Behavior of concrete columns confined by fiber composites", J. Struct. Eng., 123(5), 583-590. https://doi.org/10.1061/(ASCE)0733-9445(1997)123:5(583)
  47. Nguyen, B., Morell, C. and Baets, B.D. (2016), "Large scale distance metric learning for k-nearest neighbors regression", Neurocomput., 214, 805-814. https://doi.org/10.1016/j.neucom.2016.07.005
  48. Nielsen, M. (2016), Neural Networks and Deep Learning.
  49. Parvin, A. and Jamwal, A.S. (2005), "Effects of wrap thickness and ply configuration on composite-confined concrete cylinders", Compos. Struct., 67(4), 437-442. https://doi.org/10.1016/j.compstruct.2004.02.002
  50. Pessiki, S., Harries, K.A., Kestner, J.T., Sause, R. and Ricles, J.M. (2001), "Axial behaviour of reinforced concrete columns confined with FRP jackets", Compos. Constr., 5(4), 237-245. https://doi.org/10.1061/(ASCE)1090-0268(2001)5:4(237)
  51. Quinlan, J. (1986), "Induction of decision trees", Mach. Learn., 1(1), 81-106. https://doi.org/10.1007/BF00116251
  52. Richart, F.E., Brandtzaeg, A. and Brown, R.L. (1929), "The failure of plain and spirally reinforced concrete in compression", Ph.D. Dissertation, University of Illinois, Urbana, U.S.A.
  53. Sadeghian, P., Rahai, A.R. and Ehsani, M.R. (2008), "Numerical modeling of concrete cylinders confined with CFRP composites", J. Reinfor. Plast. Compos., 27(12), 1309-1321. https://doi.org/10.1177/0731684407084212
  54. Saridemir, M. (2009), "Prediction of compressive strength of concretes containing metakaolin and silica fume by artificial neural networks", Adv. Eng. Soft., 40(5), 350-355. https://doi.org/10.1016/j.advengsoft.2008.05.002
  55. Smola, A. and Scholkopf, B. (2004), "A tutorial on support vector regression", Stat. Comput., 14(3), 199-222. https://doi.org/10.1023/B:STCO.0000035301.49549.88
  56. Souza, A.M.F. and Soares, F.M. (2016), Neural Network Programming with Java, Packt Publishing Ltd, Birmingham, U.K.
  57. Teng, J.G., Yu, T., Wong, Y.L. and Dong, S.L. (2007), "Hybrid FRP-concrete-steel tubular columns: Concept and behavior", Constr. Build. Mater., 21(4), 846-854. https://doi.org/10.1016/j.conbuildmat.2006.06.017
  58. Topcu, I.B. and Saridemir, M. (2008), "Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic", Comput. Mater. Sci., 41(3), 305-311. https://doi.org/10.1016/j.commatsci.2007.04.009
  59. Toutanji, H.A. (1999), "Stress-strain characteristics of concrete columns externally confined with advanced fiber composites sheets", ACI Mater. J., 96(3), 397-404.
  60. Toutanji, H.A. and Deng, Y. (2001), "Strength and durability performance of concrete axially loaded members confined with AFRP composites sheets", Compos. Part B: Eng., 33(4), 255-261. https://doi.org/10.1016/S1359-8368(02)00016-1
  61. Vapnik, V.N. (1998), Statistical Learning Theory, Wiley, New York, U.S.A.
  62. Yang, L., Dong, L. and Bi, X. (2016), "An improved location difference of multiple distances based nearest neighbors searching algorithm", Optik-J. Light Electr. Opt., 127(22), 10838-10843. https://doi.org/10.1016/j.ijleo.2016.08.091

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