Structural integrity identification based on smart materials and neural networks
Autor(a) principal: | |
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Data de Publicação: | 2000 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/219234 |
Resumo: | This paper presents a non-model based technique to detect, locate, and characterize structural damage by combining the impedance-based structural health monitoring technique with an artificial neural network. The impedance-based structural health monitoring technique, which utilizes the electromechanical coupling property of piezoelectric materials, has shown engineering feasibility in a variety of practical field applications. Relying on high frequency structural excitations (typically >30 kHz), this technique is very sensitive to minor structural changes in the near field of the piezoelectric sensors. In order to quantitatively assess the state of structures, two sets of artificial neural networks, which utilize measured electrical impedance signals for input patterns, were developed. By employing high frequency ranges and by incorporating neural network features, this technique is able to detect the damage in its early stage and to estimate the nature of damage without prior knowledge of the model of structures. The paper concludes with an experimental example, an investigation on a massive quarter scale model of a steel bridge section, in order to verify the performance of this proposed methodology. |
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Repositório Institucional da UNESP |
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2946 |
spelling |
Structural integrity identification based on smart materials and neural networksThis paper presents a non-model based technique to detect, locate, and characterize structural damage by combining the impedance-based structural health monitoring technique with an artificial neural network. The impedance-based structural health monitoring technique, which utilizes the electromechanical coupling property of piezoelectric materials, has shown engineering feasibility in a variety of practical field applications. Relying on high frequency structural excitations (typically >30 kHz), this technique is very sensitive to minor structural changes in the near field of the piezoelectric sensors. In order to quantitatively assess the state of structures, two sets of artificial neural networks, which utilize measured electrical impedance signals for input patterns, were developed. By employing high frequency ranges and by incorporating neural network features, this technique is able to detect the damage in its early stage and to estimate the nature of damage without prior knowledge of the model of structures. The paper concludes with an experimental example, an investigation on a massive quarter scale model of a steel bridge section, in order to verify the performance of this proposed methodology.UNESP, Ilha SolteiraUNESP, Ilha SolteiraUniversidade Estadual Paulista (UNESP)Lopes, Vicente [UNESP]Park, Gyuhae [UNESP]Cudney, Harley H. [UNESP]Inman, Daniel J. [UNESP]2022-04-28T18:54:30Z2022-04-28T18:54:30Z2000-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article510-515Proceedings of the International Modal Analysis Conference - IMAC, v. 1, p. 510-515.1046-6770http://hdl.handle.net/11449/2192342-s2.0-0033885910Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings of the International Modal Analysis Conference - IMACinfo:eu-repo/semantics/openAccess2022-04-28T18:54:30Zoai:repositorio.unesp.br:11449/219234Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:02:54.355011Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Structural integrity identification based on smart materials and neural networks |
title |
Structural integrity identification based on smart materials and neural networks |
spellingShingle |
Structural integrity identification based on smart materials and neural networks Lopes, Vicente [UNESP] |
title_short |
Structural integrity identification based on smart materials and neural networks |
title_full |
Structural integrity identification based on smart materials and neural networks |
title_fullStr |
Structural integrity identification based on smart materials and neural networks |
title_full_unstemmed |
Structural integrity identification based on smart materials and neural networks |
title_sort |
Structural integrity identification based on smart materials and neural networks |
author |
Lopes, Vicente [UNESP] |
author_facet |
Lopes, Vicente [UNESP] Park, Gyuhae [UNESP] Cudney, Harley H. [UNESP] Inman, Daniel J. [UNESP] |
author_role |
author |
author2 |
Park, Gyuhae [UNESP] Cudney, Harley H. [UNESP] Inman, Daniel J. [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Lopes, Vicente [UNESP] Park, Gyuhae [UNESP] Cudney, Harley H. [UNESP] Inman, Daniel J. [UNESP] |
description |
This paper presents a non-model based technique to detect, locate, and characterize structural damage by combining the impedance-based structural health monitoring technique with an artificial neural network. The impedance-based structural health monitoring technique, which utilizes the electromechanical coupling property of piezoelectric materials, has shown engineering feasibility in a variety of practical field applications. Relying on high frequency structural excitations (typically >30 kHz), this technique is very sensitive to minor structural changes in the near field of the piezoelectric sensors. In order to quantitatively assess the state of structures, two sets of artificial neural networks, which utilize measured electrical impedance signals for input patterns, were developed. By employing high frequency ranges and by incorporating neural network features, this technique is able to detect the damage in its early stage and to estimate the nature of damage without prior knowledge of the model of structures. The paper concludes with an experimental example, an investigation on a massive quarter scale model of a steel bridge section, in order to verify the performance of this proposed methodology. |
publishDate |
2000 |
dc.date.none.fl_str_mv |
2000-01-01 2022-04-28T18:54:30Z 2022-04-28T18:54:30Z |
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 |
Proceedings of the International Modal Analysis Conference - IMAC, v. 1, p. 510-515. 1046-6770 http://hdl.handle.net/11449/219234 2-s2.0-0033885910 |
identifier_str_mv |
Proceedings of the International Modal Analysis Conference - IMAC, v. 1, p. 510-515. 1046-6770 2-s2.0-0033885910 |
url |
http://hdl.handle.net/11449/219234 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Proceedings of the International Modal Analysis Conference - IMAC |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
510-515 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128599437344768 |