Neural networks for modelling ultimate pure bending of steel circular tubes

Journal of Constructional Steel Research

Volumn 64, Issue 6, 2008, Pages 624-633

  Shahin, Mohamed ^a   Elchalakani, Mohamed ^b  

^a CURTIN UNIVERSITY   (Australia)

^b VICTORIA UNIVERSITY   (Australia)

Author keywords

Back propagation; Neural networks; Pure bending; Steel circular tubes

Indexed keywords

BACKPROPAGATION; BENDING (FORMING); NEURAL NETWORKS; SENSITIVITY ANALYSIS; SPREADSHEETS; TUBES (COMPONENTS);

PURE BENDING; SPREADSHEET PROGRAMMING; STEEL CIRCULAR TUBES;

STEEL;

EID: 41549111620     PISSN: 0143974X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jcsr.2007.12.001     Document Type: Article

References (33)

1. Calladine C.R. Theory of shell structures (1988), Cambridge University Press, Cambridge
2. Sherman DR. Inelastic flexural buckling of cylinders, "steel structures". In: Pavlovic MN, editor. Proceedings of the invited papers for the international conference of recent research advance and their applications to design budva. 1986
3. Ades C.S. Bending strength of tubing in the plastic range. Journal of Aerospace Science 24 (1957) 605-610
4. Elchalakani M., Zhao X.L., and Grzebieta R.H. Plastic mechanism analysis of circular tubes under pure bending. International Journal of Mechanical Sciences 44 6 (2002) 1117-1143
5. Karam G.N. On the ovalisation in bending of nylon and plastic tubes. International Journal of Pressure Vessels and Piping 58 2 (1994) 147-149
6. Kogakusi K.I. Failure of thin circular tubes under combined bending and internal or external pressure. Journal of Japanese Society of Aerospace Engineers 7 (1940) 1109
7. Mamalis A.G., Manolakas D.E., Baldoukas A.K., and Viegelahn G.L. Deformation characteristics of crashworthy thin walled steel tubes subjected to bending Proceedings of Institute of Mechanical Engineers. Journal of Mechanical Science 203 (1989) 411-417
8. Guzelbey I.H., Cevik A., and Erklig A. Prediction of web crippling strength of cold-formed steel sheeting using neural networks. Journal of Constructional Steel Research 62 10 (2006) 962-973
9. Guzelbey I.H., Cevik A., and Gogus M.T. Prediction of rotation capacity of wide flange beams using neural networks. Journal of Constructional Steel Research 62 10 (2006) 950-961
10. Jung S., and Ghaboussi J. Neural network constitutive model for rate-dependent materials. Computers and Structures 84 15-16 (2006) 955-963
11. Sakla S.S., and Ashour A.F. Prediction of tensile capacity of single adhesive anchors using neural networks. Computers and Structures 83 21-22 (2005) 1792-1803
12. Rumelhart D.E., Hinton G.E., and Williams R.J. Learning internal representation by error propagation. In: Rumelhart D.E., and McClelland J.L. (Eds). Parallel distributed processing (1986), MIT Press, Cambridge
13. Zurada J.M. Introduction to artificial neural systems (1992), West Publishing Company, St. Paul
14. Fausett L.V. Fundamentals neural networks: Architecture, algorithms, and applications (1994), Prentice-Hall, Englewood Cliffs, New Jersey
15. Neusciences. Neuframe Version 4.0 (2000), Neusciences Corp., Southampton (Hampshire)
16. Sherman D.R. Tests of circular tubes in bending. Journal of Structural Engineering ASCE 102 ST11 (1976) 2181-2195
17. Schilling G.S. Buckling strength of circular tubes. Journal of Structural Division ASCE 91 ST3 (1965) 325-348
18. Jirsa JO, HFook-Hoy L, Wilhoit JC, Merwin JE. Ovaling of pipelines under pure bending. In: Proceedings of the offshore technology conference. 1972
19. Korol R.M., and Huboda J. Plastic behaviour of hollow structural sections. Journal of Structural Division ASCE 98 ST9 (1972) 1007-1023
20. Elchalakani M., Zhao X.L., and Grzebieta R.H. Plastic slenderness limit for cold-formed circular hollow sections. Australian Journal of Structural Engineering 3 3 (2002) 127-139
21. Elchalakani M., Zhao X.L., and Grzebieta R.H. Bending tests to determine slenderness limits for cold-formed circular hollow sections. Journal of Constructional Steel Research 58 11 (2002) 1407-1430
22. Elchalakani M., Zhao X.L., and Grzebieta R.H. Cyclic bending tests to determine fully ductile slenderness limits for cold-formed circular hollow sections. Journal of Structural Engineering ASCE 127 7 (2004) 1001-1010
23. Elchalakani M., Zhao X.L., and Grzebieta R.H. Variable amplitude cyclic bending tests to determine fully ductile slenderness limits for cold-formed circular hollow sections. Engineering Structures 28 9 (2006) 1223-1235
24. Masters T. Practical neural network recipes in C ++ (1993), Academic Press, San Diego (California)
25. Shahin M.A., Maier H.R., and Jaksa M.B. Data division for developing neural networks applied to geotechnical engineering. Journal of Computing in Civil Engineering ASCE 18 2 (2004) 105-114
26. Tokar S.A., and Johnson P.A. Rainfall-runoff modelling using artificial neural networks. Journal of Hydrologic Engineering 4 3 (1999) 232-239
27. Hornik K., Stinchcombe M., and White H. Multilayer feedforward networks are universal approximators. Neural Networks 2 (1989) 359-366
28. Cybenko G. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems 3 (1989) 303-314
29. Caudill M. Neural networks primer, Part III. AI Expert 3 6 (1988) 53-59
30. Eurocode 3. Design of steel structures, Part 1.1, general rules and rules for buildings: ENV 1993 1-1, 1992
31. Standards Association of Australia. Cold-formed steel structures. Sydney (Australia): Australian/New Zealand Standard AS/NZS 4600, 1996
32. Standards Association of Australia. SAA steel structures code. Sydney (Australia): AS4100, 1998
33. AISC. Load and resistance factor design specification for structural steel buildings (1999), American Institute of Steel Construction Inc., Chicago