Neural network for constitutive modelling in finite element analysis

  • A. A. Javadi University of Exeter
  • T. P. Tan University of Exeter
  • M. Z Zhang Shanghai University

Abstract

Finite element method has, in recent years, been widely used as a powerful tool in analysis of engineering problems. In this numerical analysis, the behavior of the actual material is approximated with that of an idealized material that deforms in accordance with some constitutive relationships. Therefore, the choice of an appropriate constitutive model, which adequately describes the behavior of the material, plays a significant role in the accuracy and reliability of the numerical predictions. Several constitutive models have been developed for various materials. Most of these models involve determination of material parameters, many of which have no physical meaning [1, 2]. In this paper a neural network-based finite element analysis will be presented for modeling engineering problems. The methodology involves incorporation of neural network in a finite element program as a substitute to conventional constitutive material model. Capabilities of the presented methodology will be illustrated by application to practical engineering problems. The results of the analyses will be compared to those obtained from conventional constitutive models.

Keywords

finite element, neural network, constitutive modelling, soil,

References

[1] A. A. Javadi, M. Zhang. An intelligent finite element method for analysis of geotechnical problems. In: Proceedings of 5th World Congress on Computational Mechanics, Vienna, Austria, 2002.
[2] H.S. Shin, G. N. Pande. On self-learning finite element codes based on response of structures. Computers and Geotechnics, 27: 161- 171, 2000.
[3] I. M. Smith, D. V. Griffiths. Programming the Finite Element Method. John Wiley & Sons Ltd., England, 1998.
[4] J. M. Duncan, C. Y. Chang. Nonlinear analysis of stress and strain in soils. ASCE Journal of Soil Mechanics and Foundations Division, SM5: 1629-1653, 1970.
Published
Jan 26, 2023
How to Cite
JAVADI, A. A.; TAN, T. P.; ZHANG, M. Z. Neural network for constitutive modelling in finite element analysis. Computer Assisted Methods in Engineering and Science, [S.l.], v. 10, n. 4, p. 523-529, jan. 2023. ISSN 2956-5839. Available at: <https://cames.ippt.pan.pl/index.php/cames/article/view/1062>. Date accessed: 14 nov. 2024.
Section
Articles