Smart Structures and Systems

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Fuzzy modelling approach for shear strength prediction of RC deep beams

  • Mohammadhassani, Mohammad (Department of Structural Engineering, University of Malaya) ;
  • Saleh, Aidi MD. (Malaysian public work department) ;
  • Suhatril, M (Department of Civil Engineering, University of Malaya) ;
  • Safa, M. (Department of Civil Engineering, University of Malaya)
  • Received : 2014.07.15
  • Accepted : 2014.12.30
  • Published : 2015.09.25
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Abstract

This study discusses the use of Adaptive-Network-Based-Fuzzy-Inference-System (ANFIS) in predicting the shear strength of reinforced-concrete deep beams. 139 experimental data have been collected from renowned publications on simply supported high strength concrete deep beams. The results show that the ANFIS has strong potential as a feasible tool for predicting the shear strength of deep beams within the range of the considered input parameters. ANFIS's results are highly accurate, precise and therefore, more satisfactory. Based on the Sensitivity analysis, the shear span to depth ratio (a/d) and concrete cylinder strength ($f_c^{\prime}$) have major influence on the shear strength prediction of deep beams. The parametric study confirms the increase in shear strength of deep beams with an equal increase in the concrete strength and decrease in the shear span to-depth-ratio.

Keywords

References

  1. Akib S., Mohammadhassani M. and Jahangirzadeh A. (2014), "Application of ANFIS and LR in prediction of scour depth in bridges", Comput. Fluids., 91, 77-86. https://doi.org/10.1016/j.compfluid.2013.12.004
  2. Akrami S.A., El-Shafie A. and Jaafar O. (2013), "Improving rainfall forecasting efficiency using modified adaptive neuro-fuzzy inference sSystem (MANFIS)", Water Resour. Manag., 27(9), 3507-3523. https://doi.org/10.1007/s11269-013-0361-9
  3. Bilgehan, M. (2011), "Comparison of ANFIS and NN models-with a study in critical buckling load estimation", Appl. Soft Comput., 11(4), 3779-3791. https://doi.org/10.1016/j.asoc.2011.02.011
  4. Braestrup M.W. and Nielsen M.P. (1983), Plastic methods of analysis and design, In Handbook of Structural Concrete, London, Pitman.
  5. CIRIA Guide 2. (1977), Construction Industry research and Information association. the design of deep beams in reinforced concrete, Ove Arup & Partners London.
  6. Herrera, F. and Lozano, M. (2003), "Fuzzy adaptive genetic algorithm: design, taxonomy, and future directions", Soft Comput., 7(8), 545-562. https://doi.org/10.1007/s00500-002-0238-y
  7. Jang, J.S.R. (1993), "Adaptive network based fuzzy inference system", IEEE T. Syst., Man Cy., 23, 665-685. https://doi.org/10.1109/21.256541
  8. Kao, C.Y. and Hung, S.L. (2003), "Detection of structural damage via free vibration responses generated by approximating artificial neural networks", Comput. Struct., 81(28-29), 2631-2644. https://doi.org/10.1016/S0045-7949(03)00323-7
  9. Khaleie, S. and Fasanghari, M. (2012), "An intuitionistic fuzzy group decision making method using entropy and association coefficient", Soft Comput., 16(7), 1197-1211. https://doi.org/10.1007/s00500-012-0806-8
  10. Kong, F., Robins, P. and Cole, D. (1970), "Web reinforcement effects on deep beams", ACI Struct. J., 67(12), 1010-1017.
  11. Lin, C.T., Lin, C.T. and Lee, C.S.G. (1996), Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems, Prentice Hall PTR, 797.
  12. Lu, W.Y., Hwang, S.J. and Lin, I.J. (2010), "Deflection prediction for reinforced concrete deep beams", Comput. Concrete, 7(1), 1-16. https://doi.org/10.12989/cac.2010.7.1.001
  13. Mamdani, E. and Assilian, S. (1975), "An experiment in linguistic synthesis with a fuzzy logic controller", Int. J. Man. Mach. Stud., 7(1), 1-13. https://doi.org/10.1016/S0020-7373(75)80002-2
  14. Mashrei, M.A., Abdulrazzaq, N., Abdalla, T.Y. and Rahman, M.S. (2010), "Neural networks model and adaptive neuro-fuzzy inference system for predicting the moment capacity of ferrocement members", Eng. Struct., 32(6), 1723-1734. https://doi.org/10.1016/j.engstruct.2010.02.024
  15. Mohammadhassani, M., Jumaat, M.Z., Jameel, M., Badiee, H. and Arguman, A. (2011), "Ductility and performance assessment of high strength self compacting concrete (HSSCC) deep beams an experimental investigation", Nucl. Eng. Des., 241, 2060-2067. https://doi.org/10.1016/j.nucengdes.2011.02.022
  16. Mohammadhassani, M., Jumaat, M.Z., Ashour, A. and Mohameed, J. (2011a), "Failure modes and serviceability of high strength self compacting concrete deep beams", Eng. Fail. Anal., 18(8), 2272-2281. https://doi.org/10.1016/j.engfailanal.2011.08.003
  17. Mohammadhassani, M., Nezamabadi-Pour, H., Jumaat, M.Z., Jameel, M. and Arumugam, A.M.S. (2013), "Application of Artificial Neural Network (ANN) and Linear Regressions (LR) in predicting the deflection of concrete deep beams", Comput. Concrete., 11(3).
  18. Mohammadhassani, M., Nezamabadi-Pour, H., Jumaat, M.Z., Jameel, M., Hakim, S.J.S. and Zargar, M. (2013a), "Application of the ANFIS model in deflection prediction of concrete deep beam", Struct. Eng. Mech., 45(3), 319-332.
  19. Moller, B., Liebschera, M., Schweizerhofb, K., Matternb, S. and Blankenhornb, G. (2008), "Structural collapse simulation under consideration of uncertainty - Improvement of numerical efficiency", Comput. Struct., 86(19-20), 1875-1884. https://doi.org/10.1016/j.compstruc.2008.04.011
  20. Nielsen, M.P. (1971), "On the strength of reinforced concrete discs. copenhagen, acta polytechnica scandinavica", Civil Engineering and Building Construction Series.
  21. Oh, J.K. and Shin, S.W. (2001), "Shear strength of reinforced high strength concrete deep beam", ACI Struct. J., 8, 164-173.
  22. Pal, M. and Deswal, S. (2011), "Support vector regression based shear strength modelling of deep beams", Comput. Struct., 89(13-14), 1430-1439. https://doi.org/10.1016/j.compstruc.2011.03.005
  23. Sanad, A. and Saka, M.P. (2001), "Prediction of ultimate strength of reinforced concrete deep beams by neural networks", J. Struct. Eng. - ASCE, 127(7), 818-828. https://doi.org/10.1061/(ASCE)0733-9445(2001)127:7(818)
  24. Smith, K.N. and Vantsiotis, A.S. (1982), "Shear strength of deep beams", Amarican concrete institute (ACI), 79, 201-213.
  25. Takagi, T. and Sugeno, M. (1985), "Fuzzy identification of systems and its applications to modeling", IEEE T. Syst. Man Cy., 35, 116-132.
  26. Tan, K., Kong, F., Teng, S. and Guan, L. (1995), "High-strength concrete deep beams with effective span and shear span variations", ACI Struct. J., 92(4), 395-405.
  27. Tan, K., Kong, F., Teng, S. and Weng, L. (1997), "Effect of web reinforcement on high-strength concrete deep beams", ACI Struct. J., 94(5), 572-582.
  28. Tan, K. and Lu, H. (1999), "Shear behavior of large reinforced concrete deep beams and code comparisons", ACI Struct. J., 96(5), 836-845.
  29. Yang, K.H., Chung, H.S., Lee, E.T. and Eun, H.C. (2003), "Shear characteristics of high-strength concrete deep beams without shear reinforcements", Eng. Struct., 25(10), 1343-1352. https://doi.org/10.1016/S0141-0296(03)00110-X
  30. Yang, K., Eun, H., Lee, E. and Chung, H. (2006), "The influence of web openings on the structural behaviour of reinforced high-strength concrete deep beams", Eng. Struct., 28(13), 1825-1834. https://doi.org/10.1016/j.engstruct.2006.03.021
  31. Zadeh, L.A. (1965), "Fuzzy sets. Inform Control", 8, 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X
  32. Zhang, N. and Tan, K.H. (2007), "Size effect in RC deep beams: Experimental investigation and STM verification", Eng. Struct., 29(12), 3241-3254. https://doi.org/10.1016/j.engstruct.2007.10.005

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