Structural Engineering and Mechanics

  • 제29권1호
  • /
  • Pages.77-89
  • /
  • 2008
  • /
  • 1225-4568(pISSN)
  • /
  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Shear lag prediction in symmetrical laminated composite box beams using artificial neural network

  • Chandak, Rajeev (Department of Civil Engineering, Indian Institute of Technology) ;
  • Upadhyay, Akhil (Department of Civil Engineering, Indian Institute of Technology) ;
  • Bhargava, Pradeep (Department of Civil Engineering, Indian Institute of Technology)
  • 투고 : 2007.04.05
  • 심사 : 2008.02.18
  • 발행 : 2008.05.10
⟨ 이전 논문 다음 논문 ⟩

초록

Presence of high degree of orthotropy enhances shear lag phenomenon in laminated composite box-beams and it persists till failure. In this paper three key parameters governing shear lag behavior of laminated composite box beams are identified and defined by simple expressions. Uniqueness of the identified key parameters is proved with the help of finite element method (FEM) based studies. In addition to this, for the sake of generalization of prediction of shear lag effect in symmetrical laminated composite box beams a feed forward back propagation neural network (BPNN) model is developed. The network is trained and tested using the data base generated by extensive FEM studies carried out for various b/D, b/tF, tF/tW and laminate configurations. An optimum network architecture has been established which can effectively learn the pattern. Computational efficiency of the developed ANN makes it suitable for use in optimum design of laminated composite box-beams.

키워드

참고문헌

  1. El Kadi, H. (2006), "Modeling the mechanical behavior of fiber-reinforced polymeric composite materials using artificial neural networks-A review", Compos. Struct., 73(1), 1-23 https://doi.org/10.1016/j.compstruct.2005.01.020
  2. Evans, H.R. and Taherian, A.R. (1980), "A design aid for shear lag calculations", Proc. Inst. Civil Eng., Part 2, 69, 403-424
  3. Fafitis, A. and Rong, A.Y. (1995), "Analysis of thin-walled box girders by parallel processing", Thin Wall. Struct., 21, 233-240 https://doi.org/10.1016/0263-8231(94)00003-I
  4. Hagan, M.T. and Menhaj, M. (1994), "Training feed forward networks with the Marquardt algorithm", IEEE T. Neural Networ., 5(6), 989-993 https://doi.org/10.1109/72.329697
  5. Hagan, M.T., Demuth, H.B. and Beale, M.H. (1996) Neural Network Design, PWS Publishing, Boston (MA)
  6. Karman, Th.V. (1924), Die Mittragende Poreite. Springer, Berlin
  7. Kartalopoulos, S.V. (2000), Understanding Neural Networks and Fuzzy Logic: Basic Concepts and Applications. Prentice Hall of India, Delhi
  8. Koker, R., Altinkok, N. and Demir, A. (2007), "Neural network based prediction of mechanical properties of particulate reinforced metal matrix composites using various training algorithms", Mater. Des., 28(2), 616-627 https://doi.org/10.1016/j.matdes.2005.07.021
  9. Kristek, V. (1979), "Folded plate approach to analysis of shear wall systems and frame structures", Proc. Inst. Civil Eng., Part 2, 67, 1065-1075
  10. Kristek, V., Studnicka, J. and Skaloud, M. (1981), "Shear lag in wide flanges of steel bridges", ACTA Tech. CSAV, 26, 464-488
  11. Lopez-Anido Roberto and GangaRao, H.V.S. (1996) "Warping solution for shear lag in thin-walled orthotropic composite beams", J. Eng. Mech., ASCE, 122(5), 449-457 https://doi.org/10.1061/(ASCE)0733-9399(1996)122:5(449)
  12. Luo, Q.Z., Li, Q.S. and Tang, J. (2002), "Shear lag in box girder bridges", J. ridge Eng., ASCE, 7(5), 308-313 https://doi.org/10.1061/(ASCE)1084-0702(2002)7:5(308)
  13. MATLAB Neural Networks Toolbox of MathWorks. Available from: www.mathworks.com
  14. Mishra, A.K. and Upadhyay, A. (2004), "Column design using ANN", Indian Concrete Institute Journal, July-September, 17-19
  15. Nagaraj, V. and GangaRao, H.V.S. (1997) "Static behavior of pultruded GFRP beams", J. Comp. Const., ASCE, 1(3), 120-129 https://doi.org/10.1061/(ASCE)1090-0268(1997)1:3(120)
  16. Nakai, H. and Yoo, C.H. (1988), Analysis and Design of Curved Steel Bridges, McGraw-Hill Book Company, New York
  17. Pavlovic, M.N., Tahan, N. and Kotsovos, M.D. (1998a), "Shear lag and effective breadth in rectangular plates with material orthotropy. Part 1: Analytical formulation", Thin Wall. Struct., 30(1-4), 199-213 https://doi.org/10.1016/S0263-8231(97)00038-4
  18. Pavlovic, M.N., Tahan, N. and Kotsovos, M.D. (1998b), "Shear lag and effective breadth in rectangular plates with material orthotropy. Part 2: Typical results of parametric studies", Thin Wall. Struct., 30(1-4), 215-237 https://doi.org/10.1016/S0263-8231(97)00039-6
  19. Rajesh, A.S. (2005), "Shear lag behaviour of laminated composite box beams", M. Tech. Dissertation, Indian Institute of Technology Roorkee
  20. Reissner, E. (1946), "Analysis of sear lag in box beams by the principle of minimum potential energy", Q. Appl. Math., 6(3), 268-278
  21. Seible, F. and Scordelid, A.C. (1983), "Nonlinear analysis of multi-cell reinforced concrete box girder bridges", Eng. Struct., 5, 1045-1057
  22. Tahan, N., Pavlovic, M.N. and Kotsovos, M.D. (1997), "Shear-lag revisited: the use of single Fourier series for determining the effective breadth in plated structures", Comput. Struct., 63(4), 759-767 https://doi.org/10.1016/S0045-7949(96)00065-X
  23. Tenchev, R.T. (1992), "Shear lag in orthotropic beam flanges and plates with stiffeners", Int. J. Solids Struct., 33(9), 1317-1334
  24. Tripathy, A.K., Patel, H.J. and Pang, S.S. (1994), "Bending analysis of laminated composite box beams", J. Eng. Mater. Tech., 116, 121-129 https://doi.org/10.1115/1.2904247
  25. Turias, I.J., Gutierrez, J.M. and Galindo, P.L. (2005), "Modelling the effective thermal conductivity of an unidirectional composite by the use of artificial neural networks", Compos. Sci. Tech., 65, 609-619 https://doi.org/10.1016/j.compscitech.2004.09.018
  26. Upadhyay, A. (1998), "Optimum design of FRP box-girder bridges", Ph.D. Dissertation, Indian Institute of Technology Madras
  27. Upadhyay, A. and Kalyanaraman, V. (2003), "Simplified analysis of FRP box-girders", Compos. Struct., 59, 217- 225 https://doi.org/10.1016/S0263-8223(02)00195-2
  28. Wu, Y., Zhu, Y., Lai, Y. and Pan, W. (2002), "Analysis of shear lag and shear deformation effects in laminated composite box beams under bending loads", Compos. Struct., 55, 147-156 https://doi.org/10.1016/S0263-8223(01)00138-6
  29. Wu,Y., Lai, Y., Zhang, X. and Zhu, Y. (2004), "A finite beam element for analyzing shear lag and shear deformation effects in composite-laminated box girders", Comput. Struct., 82, 763-771 https://doi.org/10.1016/j.compstruc.2004.02.007
  30. Zhang, Z. and Friedrich, K. (2003), "Artificial neural networks applied to polymer composites: A review", Compos. Sci. Technol., 63, 2029-2044 https://doi.org/10.1016/S0266-3538(03)00106-4

피인용 문헌

  1. Identification of a suitable ANN architecture in predicting strain in tie section of concrete deep beams vol.46, pp.6, 2013, https://doi.org/10.12989/sem.2013.46.6.853
  2. Design forces for groups of six cylindrical silos by artificial neural network modelling vol.165, pp.10, 2012, https://doi.org/10.1680/stbu.10.00049
  3. Neural networks for inelastic mid-span deflections in continuous composite beams vol.36, pp.2, 2008, https://doi.org/10.12989/sem.2010.36.2.165
  4. Prediction of moments in composite frames considering cracking and time effects using neural network models vol.39, pp.2, 2008, https://doi.org/10.12989/sem.2011.39.2.267
  5. Application of artificial neural networks (ANNs) and linear regressions (LR) to predict the deflection of concrete deep beams vol.11, pp.3, 2013, https://doi.org/10.12989/cac.2013.11.3.237
  6. Explicit expressions for inelastic design quantities in composite frames considering effects of nearby columns and floors vol.64, pp.4, 2008, https://doi.org/10.12989/sem.2017.64.4.437
  7. Web buckling behavior of FRP composite box-beams: Governing parameters and their effect vol.6, pp.1, 2008, https://doi.org/10.12989/acd.2021.6.1.55