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Frontiers of Structural and Civil Engineering

Front. Struct. Civ. Eng.    2014, Vol. 8 Issue (2) : 167-177     https://doi.org/10.1007/s11709-014-0236-z
RESEARCH ARTICLE |
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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Abstract

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.
 URL:  
http://journal.hep.com.cn/fsce/EN/10.1007/s11709-014-0236-z
http://journal.hep.com.cn/fsce/EN/Y2014/V8/I2/167
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Yasser SHARIFI
Sajjad TOHIDI
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
1001
20.06470.10.998976
30.12940.10.998907
40.19400.10.998839
50.25000.10.998771
60.06470.20.994400
70.12940.20.993922
80.19400.20.993512
90.25000.20.993034
100.06470.30.982926
110.12940.30.981355
120.19400.30.979921
130.25000.30.978623
140.06470.40.961412
150.12940.40.958066
160.19400.40.954651
170.25000.40.951851
180.06470.50.927947
190.12940.50.921732
200.19400.50.915585
210.25000.50.903019
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
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