Intelligent computational solutions for constitutive modelling of materials in finite element analysis
Faramarzi, Asaad
Date: 21 October 2011
Thesis or dissertation
Publisher
University of Exeter
Degree Title
PhD in Engineering
Abstract
Over the past decades simulation techniques, and in particular finite element method,
have been used successfully to predict the response of systems across a whole range of
industries including aerospace, automotive, chemical processes, geotechnical
engineering and many others. In these numerical analyses, the behaviour of the ...
Over the past decades simulation techniques, and in particular finite element method,
have been used successfully to predict the response of systems across a whole range of
industries including aerospace, automotive, chemical processes, geotechnical
engineering and many others. In these numerical analyses, the behaviour of the actual
material is approximated with that of an idealised material that deforms in accordance
with some constitutive relationships. Therefore, the choice of an appropriate
constitutive model that adequately describes the behaviour of the material plays an
important role in the accuracy and reliability of the numerical predictions. During the
past decades several constitutive models have been developed for various materials.
In recent years, by rapid and effective developments in computational software and
hardware, alternative computer aided pattern recognition techniques have been
introduced to constitutive modelling of materials. The main idea behind pattern
recognition systems such as neural network, fuzzy logic or genetic programming is that
they learn adaptively from experience and extract various discriminants, each
appropriate for its purpose.
In this thesis a novel approach is presented and employed to develop constitutive
models for materials in general and soils in particular based on evolutionary polynomial
regression (EPR). EPR is a hybrid data mining technique that searches for symbolic
structures (representing the behaviour of a system) using genetic algorithm and
estimates the constant values by the least squares method. Stress-strain data from
experiments are employed to train and develop EPR-based material models. The
developed models are compared with some of the existing conventional constitutive
material models and its advantages are highlighted. It is also shown that the developed
EPR-based material models can be incorporated in finite element (FE) analysis.
Different examples are used to verify the developed EPR-based FE model. The results
of the EPR-FEM are compared with those of a standard FEM where conventional
constitutive models are used to model the material behaviour. These results show that
EPR-FEM can be successfully employed to analyse different structural and geotechnical
engineering problems.
Doctoral Theses
Doctoral College
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