An EPR-based self-learning approach to material modelling

Asaad Faramarzi, Akbar A. Javadi, Amir M. Alani

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)
282 Downloads (Pure)


In this paper an EPR-based self-learning method is presented for modelling the constitutive behaviour of materials using evolutionary polynomial regression (EPR). The proposed approach takes advantage of the rich stress–strain data buried in non-homogenous structural tests. The load–deformation data collected from experiment are used to iteratively train EPR-based material model using finite element simulations of the structural test. Two numerical examples are presented to illustrate the application of the proposed approach. It is shown that the EPR model gradually improves during the self-learning training and provides accurate prediction for the constitutive behaviour of the material.
Original languageEnglish
Pages (from-to)63-71
Number of pages9
JournalComputers & Structures
Early online date12 Sept 2013
Publication statusPublished - Jun 2014


  • Self-learning
  • Finite Element;
  • Evolutionary Computing
  • Material Modelling
  • EPR


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