Artificial Neural Networks in numerical modelling of composites

M. Lefik, D. P. Boso, B. A. Schrefler

Research output: Contribution to journalArticlepeer-review

106 Scopus citations


In this paper, we show some different concepts for the use of Artificial Neural Networks in modelling of composites and hierarchical structures. Starting from a relatively small set of suitable numerical experiments performed on a unit cell, a proper set of corresponding input-output data is created to train the network to identify the effective properties. Furthermore, ANN based procedures can be exploited in a multiscale analysis as a tool for the stress-strain recovery at lower levels of the hierarchical structure and/or to estimate the state of yielding of the materials. This kind of application is of great computational importance, since with material non-linearity they allow for a significantly improved computational efficiency. Finally, ANNs may be used to define the homogenised properties for a class of parameterised unit cells or when material characteristics depend upon a parameter (e.g. temperature, damage, etc.). The problem of the best ANN (or sufficiently good ANN) for each type of applications is discussed by means of the examples presented throughout the paper.

Original languageEnglish (US)
Pages (from-to)1785-1804
Number of pages20
JournalComputer Methods in Applied Mechanics and Engineering
Issue number21-26
StatePublished - May 1 2009


  • Artificial Neural Networks
  • Effective properties
  • Hierarchical structures
  • Multiscale analysis
  • Stress and strain recovery

ASJC Scopus subject areas

  • Computer Science Applications
  • Computational Mechanics
  • Mechanics of Materials
  • Mechanical Engineering
  • Physics and Astronomy(all)


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