Structural Engineering and Mechanics

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Estimation of ultimate torque capacity of the SFRC beams using ANN

  • Engin, Serkan (Department of Civil Engineering, Kocaeli University) ;
  • Ozturk, Onur (Department of Civil Engineering, Kocaeli University) ;
  • Okay, Fuad (Department of Civil Engineering, Kocaeli University)
  • Received : 2013.04.10
  • Accepted : 2014.11.06
  • Published : 2015.03.10
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Abstract

In this study, in order to propose an efficient model to predict the torque capacity of steel fiber reinforced concrete (SFRC) beams, the existing experimental data related to torsional response of beams is reviewed. It is observed that existing data neglects the effects of some parameters on the variation of torque capacity. Thus, an experimental research was also conducted to obtain the effects of neglected parameters. In the experimental study, a total of seventeen SFRC beams are tested against torsion. The parameters considered in the experiments are concrete compressive strength, steel fiber aspect ratio, volumetric ratio of steel fibers and longitudinal reinforcement ratio. The effect of each parameter is discussed in terms of torque versus unit angle of twist graphs. The data obtained from this experimental research is also combined with the data got from previous studies and employed in artificial neural network (ANN) analysis to estimate the ultimate torque capacity of SFRC beams. In addition to parameters considered in the experiments, aspect ratio of beam cross-section, yield strengths of both transverse and longitudinal reinforcements, and transverse reinforcement ratio are also defined as parameters in ANN analysis due to their significant effects observed in previous studies. Assessment of the accuracy of ANN analysis in estimating the ultimate torque capacity of SFRC beams is performed by comparing the analytical and experimental results. Comparisons are conducted in terms of root mean square error (RMSE), mean absolute error (MAE) and coefficient of efficiency ($E_f$). The results of this study revealed that addition of steel fibers increases the ultimate torque capacity of reinforced concrete beams. It is also found that ANN is a powerful method and a feasible tool to estimate ultimate torque capacity of both normal and high strength concrete beams within the range of input parameters considered.

Keywords

References

  1. Alshihri, M.M., Azmy, A.M. and El-Bisy, M.S. (2009), "Neural networks for predicting compressive strength of structural light weight concrete", Construct. Build. Mater., 23(6), 2214-2229. https://doi.org/10.1016/j.conbuildmat.2008.12.003
  2. Altun, F., Kisi, O . and Aydin, K. (2008), "Predicting the compressive strength of steel fiber added lightweight concrete using neural network", Comput. Mater. Sci., 42(2), 259-265. https://doi.org/10.1016/j.commatsci.2007.07.011
  3. 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
  4. Dantas, A.T.A., Leite, M.B. and Nagahama, K.J. (2013), "Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks", Construct. Build. Mater., 38, 717-722. https://doi.org/10.1016/j.conbuildmat.2012.09.026
  5. Demir, F. (2008), "Prediction of elastic modulus of normal and high strength concrete by artificial neural networks", Construct. Build. Mater., 22(7), 1428-1435. https://doi.org/10.1016/j.conbuildmat.2007.04.004
  6. Fang, I.K. and Shaiu, J.K. (2004), "Torsional behavior of normal and high strength concrete beams", ACI Struct. J., 101(3), 304-313.
  7. Hsu, T.T.C. (1968), "Torsion of structural concrete-behaviour of reinforced concrete rectangular members", Tors. Struct. Concrete, 18, 261-306.
  8. Lutz, L.A. and Gergely, P. (1967), "Mechanics of bond and slip of deformed bars in concrete", ACI Struct. J., 64(11), 711-721.
  9. Mansur, M.A., Nagataki, S., Lee, S.H. and Oosumimoto, Y. (1989), "Torsional response of fibrous concrete beams", ACI Struct. J., 86(1), 36-44.
  10. Mansur, M.A. and Paramasivam, P. (1982), "Steel fibre reinforced concrete beams in pure torsion", Int. J. Cement Compos. Lightw. Concrete, 4(1), 39-45. https://doi.org/10.1016/0262-5075(82)90006-9
  11. 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
  12. Narayanan, R. and Karem-Palanjian A.S. (1983), "Steel fiber reinforced concrete beams in pure torsion", Int. J. Cement Compos. Lightw. Concrete, 5(4), 235-246. https://doi.org/10.1016/0262-5075(83)90065-9
  13. Narayanan, R. and Karem-Palanjian A.S. (1986), "Torsion in beams reinforced with bars and fibers", J. Struct. Eng., 112(1), 53-66. https://doi.org/10.1061/(ASCE)0733-9445(1986)112:1(53)
  14. Olivito, R.S. and Zuccarello, F.A. (2010), "An experimental study on the tensile strength of steel fiber reinforced concrete", Compos. Part B: Eng., 41(3), 246-255. https://doi.org/10.1016/j.compositesb.2009.12.003
  15. Orangun, C.O., Jirsa, J.O. and Breen, J.E. (1977), "A revaluation of test data on development length and splices", ACI Struct. J., 74(3), 114-122.
  16. 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", Construct. Build. Mater., 20(9), 769-775. https://doi.org/10.1016/j.conbuildmat.2005.01.054
  17. Rao, T.D.G. and Seshu, D.R. (2003), "Torsion of steel fiber reinforced concrete members", Cement Concrete Res., 33(11), 1783-1788. https://doi.org/10.1016/S0008-8846(03)00174-1
  18. Rao, T.D.G. and Seshu, D.R. (2005), "Analytical model for the torsional response of steel fiber reinforced concrete members under pure torsion", Cement Concrete Compos., 27(4), 493-501. https://doi.org/10.1016/j.cemconcomp.2004.03.006
  19. Rasmussen, L.J. and Baker, G. (1995), "Torsion in reinforced normal and high-strength concrete beams-part 1: experimental test series", ACI Struct. J., 92(1), 56-62.
  20. Song, P.S. and Hwang, S. (2004), "Mechanical properties of high-strength steel fiber reinforced concrete", Construct. Build. Mater., 18(9), 669-673. https://doi.org/10.1016/j.conbuildmat.2004.04.027
  21. Topcu, I.B., Boga, A.R. and Hocaoglu, F.O. (2009), "Modeling corrosion currents of reinforced concrete using ANN", Autom. Construct., 18(2), 145-152. https://doi.org/10.1016/j.autcon.2008.07.004
  22. Topcu, I.B. and Saridemir, M. (2008a), "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
  23. Topcu, I.B. and Saridemir, M. (2008b), "Prediction of mechanical properties of recycled aggregate concretes containing silica fume using artificial neural networks and fuzzy logic", Comput. Mater. Sci., 42(1), 74-82. https://doi.org/10.1016/j.commatsci.2007.06.011
  24. Yang, I.H., Joh, C., Lee, J.W. and Kim, B.S. (2013), "Torsional behavior of ultra-high performance concrete squared beams", Eng. Struct., 56, 372-383. https://doi.org/10.1016/j.engstruct.2013.05.027
  25. Ersoy, U. (1999), Reinforced Concrete, Middle East Technical University Press, Ankara, Turkey.
  26. Haykin, S. (1999), Neural Networks - A Comprehensive Foundation, 2nd Edition, Prentice Hall, New Jersey, NJ, USA.
  27. Nilson, A.H. and Winter, G. (1991), Design of Concrete Structures, 11th Edition, McGraw-Hill, New York, NY, USA.

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