A 2D Hopfield Neural Network approach to mechanical beam damage detection

Detalhes bibliográficos
Autor(a) principal: Almeida, Juliana
Data de Publicação: 2015
Outros Autores: Alonso, Hugo, Ribeiro, Pedro, Rocha, Paula
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10773/14915
Resumo: The aim of this paper is to present a method based on a 2D Hopfield Neural Network for online damage detection in beams subjected to external forces. The underlying idea of the method is that a significant change in the beam model parameters can be taken as a sign of damage occurrence in the structural system. In this way, damage detection can be associated to an identification problem. More concretely, a 2D Hopfield Neural Network uses information about the way the beam vibrates and the external forces that are applied to it to obtain time-evolving estimates of the beam parameters at the different beam points. The neural network organizes its input information based on the Euler-Bernoulli model for beam vibrations. Its performance is tested with vibration data generated by means of a different model, namely Timonshenko's, in order to produce more realistic simulation conditions.
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spelling A 2D Hopfield Neural Network approach to mechanical beam damage detection2D Hopfield Neural NetworkEuler-Bernoulli beam modelTimoshenko beam modelDamage detectionThe aim of this paper is to present a method based on a 2D Hopfield Neural Network for online damage detection in beams subjected to external forces. The underlying idea of the method is that a significant change in the beam model parameters can be taken as a sign of damage occurrence in the structural system. In this way, damage detection can be associated to an identification problem. More concretely, a 2D Hopfield Neural Network uses information about the way the beam vibrates and the external forces that are applied to it to obtain time-evolving estimates of the beam parameters at the different beam points. The neural network organizes its input information based on the Euler-Bernoulli model for beam vibrations. Its performance is tested with vibration data generated by means of a different model, namely Timonshenko's, in order to produce more realistic simulation conditions.Springer2015-12-01T17:12:05Z2015-10-01T00:00:00Z2015-10info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/14915eng1573-082410.1007/s11045-015-0342-7Almeida, JulianaAlonso, HugoRibeiro, PedroRocha, Paulainfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-22T11:27:25Zoai:ria.ua.pt:10773/14915Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T02:50:23.556273Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A 2D Hopfield Neural Network approach to mechanical beam damage detection
title A 2D Hopfield Neural Network approach to mechanical beam damage detection
spellingShingle A 2D Hopfield Neural Network approach to mechanical beam damage detection
Almeida, Juliana
2D Hopfield Neural Network
Euler-Bernoulli beam model
Timoshenko beam model
Damage detection
title_short A 2D Hopfield Neural Network approach to mechanical beam damage detection
title_full A 2D Hopfield Neural Network approach to mechanical beam damage detection
title_fullStr A 2D Hopfield Neural Network approach to mechanical beam damage detection
title_full_unstemmed A 2D Hopfield Neural Network approach to mechanical beam damage detection
title_sort A 2D Hopfield Neural Network approach to mechanical beam damage detection
author Almeida, Juliana
author_facet Almeida, Juliana
Alonso, Hugo
Ribeiro, Pedro
Rocha, Paula
author_role author
author2 Alonso, Hugo
Ribeiro, Pedro
Rocha, Paula
author2_role author
author
author
dc.contributor.author.fl_str_mv Almeida, Juliana
Alonso, Hugo
Ribeiro, Pedro
Rocha, Paula
dc.subject.por.fl_str_mv 2D Hopfield Neural Network
Euler-Bernoulli beam model
Timoshenko beam model
Damage detection
topic 2D Hopfield Neural Network
Euler-Bernoulli beam model
Timoshenko beam model
Damage detection
description The aim of this paper is to present a method based on a 2D Hopfield Neural Network for online damage detection in beams subjected to external forces. The underlying idea of the method is that a significant change in the beam model parameters can be taken as a sign of damage occurrence in the structural system. In this way, damage detection can be associated to an identification problem. More concretely, a 2D Hopfield Neural Network uses information about the way the beam vibrates and the external forces that are applied to it to obtain time-evolving estimates of the beam parameters at the different beam points. The neural network organizes its input information based on the Euler-Bernoulli model for beam vibrations. Its performance is tested with vibration data generated by means of a different model, namely Timonshenko's, in order to produce more realistic simulation conditions.
publishDate 2015
dc.date.none.fl_str_mv 2015-12-01T17:12:05Z
2015-10-01T00:00:00Z
2015-10
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10773/14915
url http://hdl.handle.net/10773/14915
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1573-0824
10.1007/s11045-015-0342-7
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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