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Modelling stress-strain behaviour of granular soils

Modelling stress-strain behaviour of granular soils

Ahangar-Asr, A., Faramarzi, A. and Javadi, A. (2013) Modelling stress-strain behaviour of granular soils. In: Poromechanics V: Proceedings of the Fifth Biot Conference on Poromechanics. ASCE, Reston, VA, USA, pp. 1075-1081. ISBN 9780784412992 (doi:

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This paper presents a unified framework for constitutive modelling of the axial stress-volumetric strain behaviour of granular soils using an evolutionary polynomial regression technique. It 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 the model. The main advantage of the proposed model 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 behavior without any prior assumptions on the constitutive relationship. The proposed algorithm provides a transparent relationship for the constitutive material model. Merits and advantages of the proposed technique are discussed in the paper.

Item Type: Conference Proceedings
Title of Proceedings: Poromechanics V: Proceedings of the Fifth Biot Conference on Poromechanics
Additional Information: [1] This paper was first presented at the Fifth Biot Conference on Poromechanics, (Poromechanics V) held from 10-12 July 2013 in Vienna, Austria.
Uncontrolled Keywords: stress strain relations, granular media, constitutive models, porous media
Subjects: Q Science > QA Mathematics > QA76 Computer software
T Technology > TA Engineering (General). Civil engineering (General)
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
Selected for REF2021: None

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