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Frontiers of Structural and Civil Engineering

Front. Struct. Civ. Eng.    2014, Vol. 8 Issue (2) : 167-177
Lateral-torsional buckling capacity assessment of web opening steel girders by artificial neural networks – elastic investigation
Yasser SHARIFI(),Sajjad TOHIDI
Department of Civil Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran
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Bridge girders exposed to aggressive environmental conditions are subject to time-variant changes in resistance. There is therefore a need for evaluation procedures that produce accurate predictions of the load-carrying capacity and reliability of bridge structures to allow rational decisions to be made about repair, rehabilitation and expected life-cycle costs. This study deals with the stability of damaged steel I-beams with web opening subjected to bending loads. A three-dimensional (3D) finite element (FE) model using ABAQUS for the elastic flexural torsional analysis of I-beams has been used to assess the effect of web opening on the lateral buckling moment capacity. Artificial neural network (ANN) approach has been also employed to derive empirical formulae for predicting the lateral-torsional buckling moment capacity of deteriorated steel I-beams with different sizes of rectangular web opening using obtained FE results. It is found out that the proposed formulae can accurately predict residual lateral buckling capacities of doubly-symmetric steel I-beams with rectangular web opening. Hence, the results of this study can be used for better prediction of buckling life of web opening of steel beams by practice engineers.

Keywords steel I-beams      lateral-torsional buckling      finite element (FE) method      artificial neural network (ANN) approach     
Corresponding Authors: Yasser SHARIFI   
Issue Date: 19 May 2014
 Cite this article:   
Yasser SHARIFI,Sajjad TOHIDI. Lateral-torsional buckling capacity assessment of web opening steel girders by artificial neural networks – elastic investigation[J]. Front. Struct. Civ. Eng., 2014, 8(2): 167-177.
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Fig.1  Different buckling modes of steel I-beams. (a) Lateral-torsional bucklin g; (b) local buckling; (c) lateral-distortional buckling
Fig.2  Typical location of corrosion and in steel beam [17]
Fig.3  Lateral-torsional buckling of a simply supported I-beam. (a) Elevation; (b) plan on the longitudinal axis; (c) section
Fig.4  Selected picture of the ABAQUS finite element model for a steel beam with web opening
Fig.5  Lateral-torsional buckling of simply supported beam in uniform bending
Fig.6  Boundary conditions in finite element model
Fig.7  End moment simulation
Fig.8  Buckled shape for equal end moments
Fig.9  Patterns of the beam web opening
modela/hb/LMcr /Mcro
Tab.1  Lateral-torsional buckling capacity for the beam with rectangular web opening
Fig.10  Relationships between the ultimate load-carrying capacity and the opening length ratio under different opening height ratios
Fig.11  Relationships between the ultimate load-carrying capacity and the opening height ratio under different opening length ratios
Fig.12  Schematic drawing of the topology of ANN
Fig.13  Summary of the training of data set
Fig.14  Structure of the multi-layer feed forward network
Fig.15  Correlation of FEM results and ANN outputs
1 TimoshenkoS P, GereJ M. Theory of Elastic Stability. 2nd ed. New York: McGraw-Hill, 1961
2 ChenW F, LuiE M. Structural Stability: Theory and Implementation. New York: Elsevier, 1987
3 TrahairN S. The Behaviour and Design of Steel Structures. London: Chapman and Hall, 1977
4 ChajesA. Principles of Structural Stability. Englewood cliff, New jersey: Prentice-Hall Inc, 1974
5 TrahairN S, BradfordM A, NethercotD A, GardnerL. The Behaviour and Design of Steel Structures to EC3. 4nd ed. Taylor & Francis, 2008
6 KalkanI, BuyukkaragozA. A numerical and analytical study on distortional buckling of doubly-symmetric steel I-beams. Journal of Constructional Steel Research, 2012, 70: 289-297
7 GalambosT V. Guide to Stability Design Criteria for Metal Structures. 5nd ed. New York: John Wiley & Sons, 1998
8 BradfordM A, WeeA. Analysis of buckling tests on beams on seat supports. Journal of Constructional Steel Research, 1994, 28(3): 227-242
9 Standards Association of Australia. AS 4100 steel structures. Sydney, Australia, 1990
10 British Standards Institution. BS 5950 Part 1: Structural Use of Steelwork in Building. London, United Kingdom, 1990
11 ZirakianT, ShowkatiH. Experiments on distortional buckling of I-beams. Journal of Structural Engineering, 2007, 133(7): 1009-1017
12 AISC. Specification for structural steel buildings (AISC 360–05). Chicago (IL): American Institute of Steel Construction, March 9, 2005
13 SharifiY, PaikJ K. Ultimate strength reliability analysis of corroded steel-box girder bridges. Thin-walled Structures, 2011, 49(1): 157-166
14 SharifiY, PaikJ K. Environmental effects on ultimate strength reliability of corroded steel box girder bridges. Structural Longevity, 2010, 18(1): 1-20
15 SharifiY. Reliability of deteriorating steel box-girder bridges under pitting corrosion. Advanced Steel Construction, 2011, 7(3): 220-238
16 KulickiJM, PruczZ, SorgenfreiD F, MertzD R. Guidelines for Evaluating Corrosion Effects in Existing Steel Bridges. National Cooperative Highway Research Program Report333: NCHRP, 1990
17 KayserJ R, NowakA S. Capacity loss due to corrosion in steel-girder bridges. Journal of Structural Engineering, 1989, 115(6): 1525-1537
18 RedwoodR G, UenoyaM. Critical loads for webs with holes. Journal of the Structural Division, 1979, 105(ST10): 2053-2067
19 CoullA, AlvarezM C. Effect of openings on lateral buckling of beams. Journal of the Structural Division, 1980, 106(ST12): 2553-2560
20 NethercotD A, KerdalD. Lateral–torsional buckling of castellated beams. Structural Engineering Journal, 1982, 60B(3): 53-61
21 KerdalD, NethercotD A. Failure modes for castellated beams. Journal of Constructional Steel Research, 1984, 4(4): 295-315
22 ThevendranV, ShanmugamN E. Lateral buckling of narrow rectangular beams containing openings. Computers & Structures, 1992, 43(2): 247-254
23 ThevendranV, ShanmugamN E. Lateral buckling of doubly symmetric beams containing openings. Journal of Engineering Mechanics, 1991, 117(7): 1427-1441
24 ShanmugamN E, ThevendranV. Critical loads of thin-walled beams containing web openings. Thin-walled Structures, 1992, 14(4): 291-305
25 MohebkhahA. The moment-gradient factor in lateral–torsional buckling on inelastic castellated beams. Journal of Constructional Steel Research, 2004, 60(10): 1481-1494
26 AISC. Specification for Structural Steel Buildings (AISC 44–48). Chicago (IL): American Institute of Steel Construction, June 22, 2010
27 ABAQUS user's manual, version 6.9. Pawtucket, RI: Hibbit, Karlsson & Sorenson, 2005
28 HaykinS. Neural Networks: A Comprehensive Foundation. 2nd ed. New York: Macmillan College Publishing, 1998
29 Neural Network Toolbox 7 User’s Guide. Mark Hudson Beale Martin T. Hagan Howard B. Demuth, 2010
30 RumelhartD, McClellandJ. Parallel Distributed Processing. Cambridge, Mass: MIT Press, 1986
31 RojasR.Neural Networks-A Systematic Introduction. Springer-Verlag, 1996
32 PuY, MesbahiE. Application of artificial neural networks to evaluation of ultimate strength of steel panels. Engineering Structures, 2006, 28(8): 1190-1196
33 HajelaP, BerkeL. Neurobiological computational models in structural analysis and design. Computers & Structures, 1991, 41(4): 657-667
34 OkD, PuY, IncecikA. Artificial neural networks and their application to assessment of ultimate strength of plates with pitting corrosion. Ocean Engineering, 2007, 34(17-18): 2222-2230
35 GuzelbeyI H, CevikA, GögüşM T. Prediction of rotation capacity of wide flange beams using neural networks. Journal of Constructional Steel Research, 2006, 62(10): 950-961
36 FonsecaE T, VellascoP C G S, AndradeS A L, VellascoM M B R. da S Vellasco PCG, de Andrade SAL, Vellasco MMBR. Neural network evaluation of steel beam patch load capacity. Advances in Engineering Software, 2003, 34(11-12): 763-772
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