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Intelligent finite element method in geotechnical engineering

Intelligent finite element method in geotechnical engineering

Javadi, A.A., Faramarzi, A., Ahangar-Asr, A. and Mehravar, M. (2010) Intelligent finite element method in geotechnical engineering. In: Proceedings of the 18th UK Conference on Computational Mechanics (ACME-UK). ACME, Southampton, UK, pp. 89-92.

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Abstract

In this paper a new approach is presented for constitutive modelling of soils in finite element analysis. The proposed approach provides a unified framework for modelling of complex materials using evolutionary polynomial regression-based constitutive model (EPRCM), integrated in finite element analysis. Evolutionary polynomial regression (EPR) is a data mining technique that generates a transparent and structured representation of the system being studied. The development and validation of the method will be presented followed by the application to a geotechnical problem. The results of the analyses will be compared with those obtained from standard finite element analyses using conventional constitutive models. It will be shown that the EPR-based constitutive models offer an effective and unified approach to constitutive modelling of materials with complex behaviour in finite element analysis of boundary value problems.

Item Type: Conference Proceedings
Title of Proceedings: Proceedings of the 18th UK Conference on Computational Mechanics (ACME-UK)
Additional Information: This paper was first presented at the 10th Association of Computing Mechanics Engineering (ACME 2010) held from 29 - 31 March 2010 at the University of Southampton, UK.
Uncontrolled Keywords: Finite element, Constitutive modelling, Data mining
Subjects: Q Science > QA Mathematics > QA76 Computer software
T Technology > TP Chemical technology
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
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/11152

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