Javadi, Akbar A., Mehravar, Moura, Faramarzi, Asaad and Ahangar-Asr, Alireza (2009) An artificial intelligence based finite element method. ISAST Transactions on Computers and Intelligent Systems, 1 (2). pp. 1-7. ISSN 1798-2448

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Abstract

In this paper, a new approach is presented based on artificial intelligence and evolutionary computing, for constitutive modeling of materials in finite element analysis, with potential applications in different engineering disciplines. This new approach presents a unified framework for constitutive modeling 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 arameters, directly from raw experimental (or field) data. It can learn nonlinear and complex material behavior 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, ABAQUS. The application of the developed EPR-based
finite element method is illustrated through two examples and advantages of the proposed method over conventional and neural network-based FE methods are highlighted.

Item Type:	Article
Uncontrolled Keywords:	constitutive modeling, data mining, evolutionary computation, finite elements
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Pre-2014 Departments:	School of Engineering
Related URLs:	Publisher
Last Modified:	14 Oct 2016 09:23
URI:	http://gala.gre.ac.uk/id/eprint/9586

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