[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.12989/sem.2008.29.1.077

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)

Publication Information

Structural Engineering and Mechanics / v.29, no.1, 2008 , pp. 77-89 More about this Journal

Abstract

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.

Keywords

laminated composites; shear lag; effective width ratio; finite element method; artificial neural networks;

Citations & Related Records

Times Cited By Web Of Science : 2 (Related Records In Web of Science)
Times Cited By SCOPUS : 2

Reference
Cited By SCOPUS

1	Hagan, M.T., Demuth, H.B. and Beale, M.H. (1996) Neural Network Design, PWS Publishing, Boston (MA)
2	Karman, Th.V. (1924), Die Mittragende Poreite. Springer, Berlin
3	Kartalopoulos, S.V. (2000), Understanding Neural Networks and Fuzzy Logic: Basic Concepts and Applications. Prentice Hall of India, Delhi
4	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 DOI ScienceOn
5	Evans, H.R. and Taherian, A.R. (1980), "A design aid for shear lag calculations", Proc. Inst. Civil Eng., Part 2, 69, 403-424
6	Fafitis, A. and Rong, A.Y. (1995), "Analysis of thin-walled box girders by parallel processing", Thin Wall. Struct., 21, 233-240 DOI ScienceOn
7	Kristek, V. (1979), "Folded plate approach to analysis of shear wall systems and frame structures", Proc. Inst. Civil Eng., Part 2, 67, 1065-1075
8	Kristek, V., Studnicka, J. and Skaloud, M. (1981), "Shear lag in wide flanges of steel bridges", ACTA Tech. CSAV, 26, 464-488
9	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 DOI
10	Luo, Q.Z., Li, Q.S. and Tang, J. (2002), "Shear lag in box girder bridges", J. ridge Eng., ASCE, 7(5), 308-313 DOI
11	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 DOI ScienceOn
12	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 DOI ScienceOn
13	Upadhyay, A. (1998), "Optimum design of FRP box-girder bridges", Ph.D. Dissertation, Indian Institute of Technology Madras
14	Seible, F. and Scordelid, A.C. (1983), "Nonlinear analysis of multi-cell reinforced concrete box girder bridges", Eng. Struct., 5, 1045-1057
15	Tenchev, R.T. (1992), "Shear lag in orthotropic beam flanges and plates with stiffeners", Int. J. Solids Struct., 33(9), 1317-1334
16	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 DOI ScienceOn
17	Rajesh, A.S. (2005), "Shear lag behaviour of laminated composite box beams", M. Tech. Dissertation, Indian Institute of Technology Roorkee
18	Reissner, E. (1946), "Analysis of sear lag in box beams by the principle of minimum potential energy", Q. Appl. Math., 6(3), 268-278
19	MATLAB Neural Networks Toolbox of MathWorks. Available from: www.mathworks.com
20	Mishra, A.K. and Upadhyay, A. (2004), "Column design using ANN", Indian Concrete Institute Journal, July-September, 17-19
21	Nakai, H. and Yoo, C.H. (1988), Analysis and Design of Curved Steel Bridges, McGraw-Hill Book Company, New York
22	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 DOI ScienceOn
23	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 DOI ScienceOn
24	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 DOI ScienceOn
25	Upadhyay, A. and Kalyanaraman, V. (2003), "Simplified analysis of FRP box-girders", Compos. Struct., 59, 217- 225 DOI ScienceOn
26	Zhang, Z. and Friedrich, K. (2003), "Artificial neural networks applied to polymer composites: A review", Compos. Sci. Technol., 63, 2029-2044 DOI ScienceOn
27	Hagan, M.T. and Menhaj, M. (1994), "Training feed forward networks with the Marquardt algorithm", IEEE T. Neural Networ., 5(6), 989-993 DOI ScienceOn
28	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 DOI ScienceOn
29	Nagaraj, V. and GangaRao, H.V.S. (1997) "Static behavior of pultruded GFRP beams", J. Comp. Const., ASCE, 1(3), 120-129 DOI ScienceOn
30	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 DOI ScienceOn

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10	Suleyman B. Yuksel. (2012) Proceedings of the Institution of Civil Engineers - Structures and Buildings Design forces for groups of six cylindrical silos by artificial neural network modelling / 165 (10) , 567
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4	(2008) Structural engineering and mechanics : An international journal Explicit expressions for inelastic design quantities in composite frames considering effects of nearby columns and floors / 64 (4) , 437
1	(2008) Advances in computational design Web buckling behavior of FRP composite box-beams: Governing parameters and their effect / 6 (1) , 55

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1	/ Structural Engineering and Mechanics
2	/ Structural Engineering and Mechanics