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An EPR-based self-learning approach to material modelling

An EPR-based self-learning approach to material modelling

Faramarzi, Asaad, Alani, Amir M. and Javadi, Akbar A. (2014) An EPR-based self-learning approach to material modelling. Computers & Structures, 137. pp. 63-71. ISSN 0045-7949 (doi:https://doi.org/10.1016/j.compstruc.2013.06.012)

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Abstract

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.

Item Type: Article
Additional Information: [1] Published in: Computers & Structures, Volume 137, June 2014 - Special Issue Title: UK Association for Computational Mechanics in Engineering.
Uncontrolled Keywords: self-learning, finite element, evolutionary computation, material modelling, EPR
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Faculty / Department / Research Group: Faculty of Engineering & Science > Department of Engineering Science
Faculty of Engineering & Science
Related URLs:
Last Modified: 30 Jan 2017 15:11
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/9657

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