Neural network constitutive model for rate-dependent materials

Computers and Structures

Volumn 84, Issue 15-16, 2006, Pages 955-963

  Jung, Sungmoon ^a   Ghaboussi, Jamshid ^b  

^a CATERPILLAR INC   (United States)

^b University of Illinois at Urbana Champaign   (United States)

Author keywords

Constitutive modeling; Finite element; Neural networks; Rate dependency; Viscoelasticity; Viscoplasticity

Indexed keywords

CONCRETES; FINITE ELEMENT METHOD; NEURAL NETWORKS; STRAIN; STRESS ANALYSIS; VISCOELASTICITY; VISCOPLASTICITY;

CONSTITUTIVE MODELING; FINITE ELEMENT; RATE-DEPENDENCY;

MATERIALS SCIENCE;

EID: 33646361038     PISSN: 00457949     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compstruc.2006.02.015     Document Type: Article

References (26)

1. Ghaboussi J., Garrett J.H., and Wu X. Knowledge-based modeling of material behavior with neural networks. J Eng Mech-ASCE 117 1 (1991) 132-153
2. Ghaboussi J., and Sidarta D.E. New nested adaptive neural networks (NANN) for constitutive modeling. Comput Geotech 22 1 (1998) 29-52
3. Ghaboussi J., Pecknold D.A., Zhang M.F., and Haj-Ali R.M. Autoprogressive training of neural network constitutive models. Int J Numer Meth Eng 42 1 (1998) 105-126
4. Hashash Y.M.A., Jung S., and Ghaboussi J. Numerical implementation of a neural network based material model in finite element analysis. Int J Numer Meth Eng 59 7 (2004) 989-1005
5. Hashash Y.M.A., Marulanda C., Ghaboussi J., and Jung S. Systematic update of a deep excavation model using field performance data. Comput Geotech 30 6 (2003) 477-488
6. Haj-Ali R., Pecknold D.A., Ghaboussi J., and Voyiadjis G.Z. Simulated micromechanical models using artificial neural networks. J Eng Mech-ASCE 127 7 (2001) 730-738
7. Shin H.S., and Pande G.N. On self-learning finite element codes based on monitored response of structures. Comput Geotech 27 3 (2000) 161-178
8. Ellis G.W., Yao C., Zhao R., and Penumadu D. Stress-strain modeling of sands using artificial neural networks. J Geotech Eng-ASCE 121 5 (1995) 429-435
9. Furukawa T., and Yagawa G. Implicit constitutive modelling for viscoplasticity using neural networks. Int J Numer Meth Eng 43 2 (1998) 195-219
10. Al-Haik M.S., Garmestani H., and Navon I.M. Truncated-Newton training algorithm for neurocomputational viscoplastic model. Comput Method Appl Mech Eng 192 19 (2003) 2249-2267
11. Wu X. Neural Network-Based Material Modeling. In: Department of Civil Engineering. University of Illinois at Urbana-Champaign: Urbana; 1991.
12. Zhang MM. Neural network material models determined from structural tests. In: Department of civil engineering. University of Illinois at Urbana-Champaign: Urbana; 1997.
13. Sidarta DE. Neural network-based constitutive modeling of granular material. Department of civil engineering. University of Illinois at Urbana-Champaign: Urbana; 2000.
14. Hertz J.A., Krogh A., and Palmer R.G. Introduction to the theory of neural computation (1991), Perseus Publishing
15. Reed R.D., and Marks II R.J. Neural smithing: Supervised learning in feedforward artificial neural networks (1999), MIT Press
16. Press W.H., Flannery B.P., Teukolsky S.A., and Vetterling W.T. Numerical recipes in fortran (1992), Cambridge University Press
17. HKS. ABAQUS/Standard. 2002, Hibbitt, Karlsson & Sorensen, Inc.
18. Shin H.S., and Pande G.N. Enhancement of data for training neural network based constitutive models for geomaterials. In: 8th international symposium on numerical models in geomechanics - NUMOG VIII (2002), Balkema, Italy 141-146
19. Marulanda C., Butkovich J., Hashash Y.M.A., and Ghaboussi J. Constitutive model update using observed field behavior. 9th international symposium on numerical models in geomechanics - NUMOG IX (2004), Balkema, Canada
20. Haddad Y.M. Viscoelasticity of engineering materials (1995), Chapman & Hall, New York
21. Riedmiller M. Advanced supervised learning in multi-layer perceptrons - from backpropagation to adaptive learning algorithms. Comput Stand Interf 16 3 (1994) 265-278
22. Riedmiller M., and Braun H. A direct adaptive method for faster backpropagation learning: the RPROP algorithm. 1993 IEEE international conference on neural networks (1993), IEEE, San Francisco 586-591
23. Anderson T.C., Houdeshell D.M., and Gamble W.L. Construction and long-term behavior of 1/8-th scale prestressed concrete bridge Components (1972), University of Illinois, Urbana
24. Gopalakrishnan K.S., Neville A.M., and Ghali A. Creep poisson's ratio of concrete under multiaxial compression. J Am Concrete I 66 12 (1969) 1008-1020
25. Hannant D.J. Creep and creep recovery of concrete subjected to multiaxial compressive stress. J Am Concrete I 66 5 (1969) 391-394
26. Jordaan I.J., and Illston J.M. Creep of sealed concrete under multiaxial compressive stresses. Mag Concrete Res 21 69 (1969) 195-204