Dynamic modeling of magnetorheological elastomer base isolator based on extreme learning machine

Yu, Y; Li, Y; Li, J; Gu, X; Royel, S

Dynamic modeling of magnetorheological elastomer base isolator based on extreme learning machine

Yu, Y

Li, Y

Li, J

Gu, X

Royel, S

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Publication Type:: Conference Proceeding
Citation:: Mechanics of Structures and Materials: Advancements and Challenges - Proceedings of the 24th Australasian Conference on the Mechanics of Structures and Materials, ACMSM24 2016, 2017, pp. 703 - 708
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Yu, Y https://orcid.org/0000-0001-9592-8191	en_US
dc.contributor.author	Li, Y https://orcid.org/0000-0002-6720-8493	en_US
dc.contributor.author	Li, J https://orcid.org/0000-0002-2526-3048	en_US
dc.contributor.author	Gu, X https://orcid.org/0000-0003-3161-8391	en_US
dc.contributor.author	Royel, S https://orcid.org/0000-0001-6262-5282	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	Mechanics of Structures and Materials: Advancements and Challenges - Proceedings of the 24th Australasian Conference on the Mechanics of Structures and Materials, ACMSM24 2016, 2017, pp. 703 - 708	en_US
dc.identifier.isbn	9781138029934	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127689
dc.description.abstract	© 2017 Taylor & Francis Group, London. This paper presents a novel modeling method to describe the nonlinear and hysteretic characteristics of Magnetorheological Elastomer (MRE) isolator, which is a semi-active control device and used in vibration control of engineering structures such as vehicle suspension system, offshore platform and built infrastructure. In the proposed method, a new single-hidden-layer feed-forward neural network algorithm named Extreme Learning Machine (ELM) is adopted to set up the model, in which the captured responses such as displacement and velocity of the device together with applied current level are employed as model inputs while the model output is the shear force generated according to the external excitation. Finally, the experimental data are utilized to validate the performance of the proposed method.	en_US
dc.relation.ispartof	Mechanics of Structures and Materials: Advancements and Challenges - Proceedings of the 24th Australasian Conference on the Mechanics of Structures and Materials, ACMSM24 2016	en_US
dc.title	Dynamic modeling of magnetorheological elastomer base isolator based on extreme learning machine	en_US
dc.type	Conference Proceeding
utslib.for	0905 Civil Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2017 Taylor & Francis Group, London. This paper presents a novel modeling method to describe the nonlinear and hysteretic characteristics of Magnetorheological Elastomer (MRE) isolator, which is a semi-active control device and used in vibration control of engineering structures such as vehicle suspension system, offshore platform and built infrastructure. In the proposed method, a new single-hidden-layer feed-forward neural network algorithm named Extreme Learning Machine (ELM) is adopted to set up the model, in which the captured responses such as displacement and velocity of the device together with applied current level are employed as model inputs while the model output is the shear force generated according to the external excitation. Finally, the experimental data are utilized to validate the performance of the proposed method.

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http://hdl.handle.net/10453/127689