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EPR-based material modelling of soils considering volume changes

EPR-based material modelling of soils considering volume changes

Faramarzi, Asaad, Javadi, Akbar A. and Alani, Amir M. (2012) EPR-based material modelling of soils considering volume changes. Computers & Geosciences, 48. pp. 73-85. ISSN 0098-3004 (doi:https://doi.org/10.1016/j.cageo.2012.05.015)

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Abstract

In this paper an approach is presented for developing material models for soils based on evolutionary polynomial regression (EPR), taking into account its volumetric behaviour. EPR is a recently developed hybrid data mining technique that searches for structured mathematical equations (representing the behaviour of a system) using genetic algorithm and the least squares method. Stress–strain data from triaxial test are used to train and develop EPR-based material models for soil. The developed models are compared with some of the well known onventional material models. In particular, the capability of the developed EPR models in predicting volume change behaviour of soils is illustrated. It is also shown that the developed EPR-based material models can be incorporated in finite element (FE) analysis. Two geotechnical examples are presented to verify the developed EPR-based FE model (EPR-FEM). The results of the EPR-FEM are compared with those of a standard FEM where conventional constitutive models are used to describe the material behaviour. The results show that EPR-FEM can be successfully employed to analyse geotechnical engineering problems. The advantages of the proposed EPR models are highlighted.

Item Type: Article
Additional Information: [1] Available online 24 May 2012.
Uncontrolled Keywords: finite element, evolutionary computation, EPR, material modelling, volumetric behaviour Numerical modelling
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Pre-2014 Departments: School of Engineering
Related URLs:
Last Modified: 14 Oct 2016 09:23
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/9577

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