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http://dx.doi.org/10.12989/scs.2014.17.3.215

Prediction of the flexural overstrength factor for steel beams using artificial neural network  

Guneyisi, Esra Mete (Department of Civil Engineering, Gaziantep University)
D'niell, Mario (Department of Structures for Engineering and Architecture, University of Naples "Federico II")
Landolfo, Raffaele (Department of Structures for Engineering and Architecture, University of Naples "Federico II")
Mermerdas, Kasim (Department of Civil Engineering, Hasan Kalyoncu University)
Publication Information
Steel and Composite Structures / v.17, no.3, 2014 , pp. 215-236 More about this Journal
Abstract
The flexural behaviour of steel beams significantly affects the structural performance of the steel frame structures. In particular, the flexural overstrength (namely the ratio between the maximum bending moment and the plastic bending strength) that steel beams may experience is the key parameter affecting the seismic design of non-dissipative members in moment resisting frames. The aim of this study is to present a new formulation of flexural overstrength factor for steel beams by means of artificial neural network (NN). To achieve this purpose, a total of 141 experimental data samples from available literature have been collected in order to cover different cross-sectional typologies, namely I-H sections, rectangular and square hollow sections (RHS-SHS). Thus, two different data sets for I-H and RHS-SHS steel beams were formed. Nine critical prediction parameters were selected for the former while eight parameters were considered for the latter. These input variables used for the development of the prediction models are representative of the geometric properties of the sections, the mechanical properties of the material and the shear length of the steel beams. The prediction performance of the proposed NN model was also compared with the results obtained using an existing formulation derived from the gene expression modeling. The analysis of the results indicated that the proposed formulation provided a more reliable and accurate prediction capability of beam overstrength.
Keywords
experimental database; flexural overstrength; modeling; neural networks; steel beams;
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1 Arbib, M.A. (1995), Handbook of Brain Theory and NN, MIT Press, Cambridge, MA, USA.
2 AISC 341-10 (2010), Seismic Provisions for Structural Steel Buildings, American Institute of Steel Construction, Chicago, IL, USA.
3 Aleksander, I. and Morton, H. (1993), Neurons and Symbols: The Staff That Mind is Made of, Chapman and Hall, London, England.
4 Anderson, J.A. (1995), An Introduction to Neural Networks, A Bradford Book, MIT Press, Cambridge, MA, USA.
5 Boeraeve, P. and Lognard, B. (1993), "Elasto-plastic behaviour of steel frame works", J. Construct. Steel Res., 27(1-3), 3-21.   DOI
6 Brooke, N.J. and Ingham, J.M. (2011), "The effect of reinforcement strength on the overstrength factor for reinforced concrete beams", Proceedings of the Ninth Pacific Conference on Earthquake Engineering Building an Earthquake-Resilient Society, Auckland, New Zealand, April.
7 Climenhaga, J.J. (1970), "Local buckling in composite beams", Ph.D. Dissertation, University of Cambridge, Cambridge, England.
8 D'Aniello, M., Guneyisi, E.M., Landolfo, R. and Mermerdas, K. (2014), "Analytical prediction of available rotation capacity of cold-formed rectangular and square hollow section beams", Thin-Wall. Struct., 77, 141-152. DOI: 10.1016/j.tws.2013.09.015   DOI
9 Dahl, W., Langenberg, P., Sedlacek, G. and Spangemacher, R. (1992), "Elastisch-Plastisches Verhalten von Stahlkonstruktionen Anfoderungen und Werkstoffkennwerte", Doc.-Nr. 7210-Sa / 118 (91-F6.05), Rheinisch-Westfalischen Technischen Hochshule Aachen, Germany.
10 Da Silva, S.L., Rebelo, C., Nethercot, D., Marques, L., Simoes, R. and Vila Real, P.M.M. (2009), "Statistical evaluation of the lateral-torsional buckling resistance of steel I-beams, Part 2: Variability of steel properties", J. Construct. Steel Res., 65(4), 832-849.   DOI
11 D'Aniello, M., Landolfo, R., Piluso, V. and Rizzano, G. (2012), "Ultimate behavior of steel beams under non-uniform bending", J. Construct. Steel Res., 78, 144-158.   DOI
12 Della Corte, G., D'Aniello, M. and Mazzolani, F.M. (2007), "Inelastic response of shear links with axial restraints: Numerical vs. Analytical results", Proceedings of the 5th International Conference on Advances in Steel Structures, Singapore, December.
13 Della Corte, G., D'Aniello, M. and Landolfo, R. (2013), "Analytical and numerical study of plastic overstrength of shear links", J. Construct. Steel Res., 82, 19-32.   DOI   ScienceOn
14 EN 1993-1-1: Eurocode 3 (2005), Design of Steel Structures - Part 1: General Rules and Rules for Buildings, CEN (European Communities for Standardization), Brussels, Belgium.
15 Fonseca, E.T., Vellasco, P.C.G.d.S., de Andrade, S.A.L. and Vellasco, M.M.B.R. (2003), "Neural network evaluation of steel beam patch load capacity", Adv. Eng. Software, 34(11-12), 763-772.   DOI
16 Frye, M.J. and Morris, G.A. (1975), "Analysis of flexibly connected steel frames", Can. J. Civ. Eng., 2(3), 280-291.   DOI
17 Gholizadeh, S., Pirmoz, A. and Attarnejad, R. (2011), "Assessment of load carrying capacity of castellated steel beams by neural networks", J. Construct. Steel Res., 67(5), 770-779.   DOI
18 Grecea, D., Dinu, F. and Dubina, D. (2004), "Performance criteria for MR steel frames in seismic zones", J. Construct. Steel Res., 60(3-5), 739-749.   DOI
19 Gandomi, A.H., Alavi, A.H., Kazemi, S.S. and Alinia, M.M. (2009), "Behavior appraisal of steel semi-rigid joints using Linear Genetic Programming", J. Construct. Steel Res., 65(8-9), 1738-1750.   DOI
20 Gao, S., Zhang, Z. and Cao, C. (2011), "Road traffic freight volume forecast using support vector machine combining forecasting", J. Software, 6(9), 1680-1687.
21 Grubb, M.A. and Carskaddan, P.S. (1979), "AISI project 188, 97-H-045(019-4), Autostress design of highway bridges Phase 3: Initial moment-rotation tests", United States Steel Corporation Research Laboratory, USA.
22 Grubb, M.A. and Carskaddan, P.S. (1981), "AISI project 188, 97-H-045(018-1), Autostress design of highway bridges Phase 3: Moment-rotation requirements", United States Steel Corporation Research Laboratory, USA.
23 Guneyisi, E.M., D'Aniello, M., Landolfo, R. and Mermerdas, K. (2013), "A novel formulation of the flexural overstrength factor for steel beams", J. Construct. Steel Res., 90, 60-71.   DOI
24 Hakim, S.J.S. and Abdul-Razak, H. (2013), "Structural damage detection of steel bridge girder using artificial neural networks and finite element models", Steel Compos. Struct., Int. J., 14(4), 367-377.   DOI
25 Kim, S.E. and Ma, S.S. (2007), "Optimal design using genetic algorithm with nonlinear inelastic analysis", Steel Compos. Struct., Int. J., 7(6), 421-440.   DOI
26 Hayalioglu, M.S. and Degertekin, S.O. (2004), "Genetic algorithm based optimum design of non-linear steel frames with semi-rigid connections", Steel Compos. Struct., Int. J., 4(6), 453-469.   DOI
27 Kemp, A. (1985), "Interaction of plastic local and lateral buckling", ASCE J. Struct. Eng., 111(10), 2181-2196.   DOI   ScienceOn
28 Landolfo, R., D'Aniello, M., Brescia, M. and Tortorelli, S. (2011), Rotation capacity and classification criteria of steel beams. The development of innovative approaches for the design of steel-concrete structural systems - the line 5 of the ReLUIS-DPC 2005-2008 Project 37-88, Doppiavoce, Napoli, Italy.
29 Kim, D., Kim, D.H., Cui, J., Seo, H.Y. and Lee, Y.H. (2009a), "Iterative neural network strategy for static model identification of an FRP deck", Steel Compos. Struct., Int. J., 9(5), 445-455.   DOI
30 Kim, K.N., Lee, S.H. and Jung, K.S. (2009b), "Prediction on the fatigue life of butt-welded specimens using artificial neural network", Steel Compos. Struct., Int. J., 9(6), 557-568.   DOI
31 Levenberg, K. (1944), "A method for the solution of certain non-linear problems in least squares", Q. J. Appl. Math., 2(2), 164-168.
32 Lukey, A.F. and Adams, P.F. (1969), "Rotation capacity of beams under moment gradient", J Struct. Div., 95(ST 6), 1173-1188.
33 Mazzolani, F.M. and Piluso, V. (1993), "Member behavioural classes of steel beams and beam-columns", Proceedings of XIV CTA Conference, Viareggio, Italy, June.
34 Rebelo, C., Lopes, N., Simoes da Silva, L., Nethercot, D. and Vila Real, P.M.M. (2009), "Statistical evaluation of the lateral-torsional buckling resistance of steel I-beams, Part 1: Variability of the Eurocode 3 resistance model", J. Construct. Steel Res., 65(4), 818-831.   DOI
35 Mukherjee, A. and Biswas, S.N. (1997), "Artificial neural networks in prediction of mechanical behavior of concrete at high temperature", Nucl. Eng. Des., 178(3), 1-11.   DOI   ScienceOn
36 OPCM 3274 (2003), First elements in the matter of general criteria for seismic classification of the national territory and of technical codes for structures in seismic zones, Official Gazette of the Italian Republic, and further modifications, Rome, Italy.
37 Suzuki, T., Ogawa, T. and Ikaraski, K. (1994), "A study on local buckling behaviour of hybrid beams", Thin-Wall. Struct., 19(2-4), 337-351.   DOI
38 Schilling, C.G. (1988), "Moment-rotation tests of steel bridge girders", ASCE J. Struct. Eng., 114(1), 134-149.   DOI
39 Topcu, I.B. and Saridemir, M. (2008), "Prediction of mechanical properties of recycled aggregate concretes containing silica fume using artificial neural networks and fuzzy logic", Comput. Mater. Sci., 42(1), 74-82.   DOI
40 Tortorelli, S., D'Aniello, M. and Landolfo, R. (2010), "Lateral capacity of steel structures designed according to EC8 under catastrophic seismic events", Proceedings of the Final Conference COST ACTION C26: Urban Habitat Constructions under Catastrophic Events, Naples, Italy, September.
41 Wargsjo, A. (1991), "Plastisk rotationskapacitet hos svetsade stalbalkar", Licentiate Thesis, Lulea University of Technology, Sweden. [In Swedish]
42 Zhou, F. and Young, B. (2005), "Tests of cold-formed stainless steel tubular flexural members", Thin-Wall. Struct., 43(9), 1325-1337.   DOI   ScienceOn
43 Wilkinson, T. (1999), "The plastic behaviour of cold formed rectangular hollow sections", Ph.D. Thesis, Department of Civil Engineering, University of Sydney, Australia.
44 Yu, W.W. (2000), Cold Formed Steel Design, (3rd Edition), John Wiley & Sons Inc., USA.
45 Zadeh, L.A. (1994), "Soft computing and fuzzy logic", IEEE Software, 11(6), 48-56.
46 Schilling, C.G. (1994), "Moment-rotation tests of steel girders with ultracompact flanges", Proceedings of 1990 Annual Technical Session, Stability of Bridges, Structural Stability Research Council, St. Louis, MO, USA.