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An intelligent finite element method

An intelligent finite element method

Javadi, A.A., Faramarzi, A. and Ahangar-Asr, A. (2009) An intelligent finite element method. In: Proceedings of the 17th UK Conference on Computational Mechanics. Spencer Institute of Theoretical and Computational Mechanics, Unversity of Nottingham, Nottingham, UK, pp. 307-310. ISBN 9780853582557

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Abstract

This paper presents a unified framework for constitutive modelling of complex materials in finite element analysis using evolutionary polynomial regression (EPR). EPR is a data-driven method based on evolutionary computing, aimed to search for polynomial structures representing a system. A procedure is presented for construction of EPR-based constitutive model (EPRCM) and its integration in finite element procedure. The main advantage of EPRCM over conventional and neural network-based constitutive models is that it provides the optimum structure for the material constitutive model representation as well as its parameters, directly from raw experimental (or field) data. It can learn nonlinear and complex material behaviour without any prior assumption on the constitutive relationship. The proposed approach provides a transparent relationship for the constitutive material model that can readily be incorporated in a finite element model. A procedure is presented for efficient training of EPR, computing the stiffness matrix using the trained EPR model and incorporation of the EPRCM in a commercial finite element code. The application of the developed EPR-based finite element method is illustrated through an example and advantages of the proposed method over conventional and neural network-based FE methods are highlighted.

Item Type: Conference Proceedings
Title of Proceedings: Proceedings of the 17th UK Conference on Computational Mechanics
Additional Information: [1] This paper was first presented at the 17th UK Conference on Computational Mechanics (ACME-UK), held from 4-6 April 2009 in Nottingham, UK.
Uncontrolled Keywords: constitutive modelling, evolutionary computation, data mining, finite elements
Subjects: T Technology > TJ Mechanical engineering and machinery
Pre-2014 Departments: School of Engineering
School of Engineering > Department of Civil Engineering
Related URLs:
Last Modified: 14 Oct 2016 09:26
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/11154

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