Structural health evaluation by optimization techinique and artificial neural network
Autor(a) principal: | |
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Data de Publicação: | 2002 |
Outros Autores: | , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/38038 |
Resumo: | This paper presents two different approaches to detect, locate, and characterize structural damage. Both techniques utilize electrical impedance in a first stage to locate the damaged area. In the second stage, to quantify the damage severity, one can use neural network, or optimization technique. The electrical impedance-based, 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, this technique is very sensitive to minor structural changes in the near field of the piezoelectric sensors, and therefore, it is able to detect the damage in its early stage. Optimization approaches must be used for the case where a good condensed model is known, while neural network can be also used to estimate the nature of damage without prior knowledge of the model of the structure. The paper concludes with an experimental example in a welded cubic aluminum structure, in order to verify the performance of these two proposed methodologies. |
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Repositório Institucional da UNESP |
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spelling |
Structural health evaluation by optimization techinique and artificial neural networkThis paper presents two different approaches to detect, locate, and characterize structural damage. Both techniques utilize electrical impedance in a first stage to locate the damaged area. In the second stage, to quantify the damage severity, one can use neural network, or optimization technique. The electrical impedance-based, 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, this technique is very sensitive to minor structural changes in the near field of the piezoelectric sensors, and therefore, it is able to detect the damage in its early stage. Optimization approaches must be used for the case where a good condensed model is known, while neural network can be also used to estimate the nature of damage without prior knowledge of the model of the structure. The paper concludes with an experimental example in a welded cubic aluminum structure, in order to verify the performance of these two proposed methodologies.UNESP, Dept Mech Engn, BR-13385000 Llha Solteira, SP, BrazilUNESP, Dept Mech Engn, BR-13385000 Llha Solteira, SP, BrazilSoc Experimental Mechanics IncUniversidade Estadual Paulista (Unesp)Lopes, VTurra, A. E.Muller-Slany, H. H.Brunzel, F.Inman, D. J.2014-05-20T15:28:10Z2014-05-20T15:28:10Z2002-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject484-490Proceedings of Imac-xx: Structural Dynamics Vols I and Ii. Bethel: Soc Experimental Mechanics Inc., v. 4753, p. 484-490, 2002.0277-786Xhttp://hdl.handle.net/11449/38038WOS:000176646000070Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings of Imac-xx: Structural Dynamics Vols I and Iiinfo:eu-repo/semantics/openAccess2024-07-04T20:06:42Zoai:repositorio.unesp.br:11449/38038Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:08:54.693701Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Structural health evaluation by optimization techinique and artificial neural network |
title |
Structural health evaluation by optimization techinique and artificial neural network |
spellingShingle |
Structural health evaluation by optimization techinique and artificial neural network Lopes, V |
title_short |
Structural health evaluation by optimization techinique and artificial neural network |
title_full |
Structural health evaluation by optimization techinique and artificial neural network |
title_fullStr |
Structural health evaluation by optimization techinique and artificial neural network |
title_full_unstemmed |
Structural health evaluation by optimization techinique and artificial neural network |
title_sort |
Structural health evaluation by optimization techinique and artificial neural network |
author |
Lopes, V |
author_facet |
Lopes, V Turra, A. E. Muller-Slany, H. H. Brunzel, F. Inman, D. J. |
author_role |
author |
author2 |
Turra, A. E. Muller-Slany, H. H. Brunzel, F. Inman, D. J. |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Lopes, V Turra, A. E. Muller-Slany, H. H. Brunzel, F. Inman, D. J. |
description |
This paper presents two different approaches to detect, locate, and characterize structural damage. Both techniques utilize electrical impedance in a first stage to locate the damaged area. In the second stage, to quantify the damage severity, one can use neural network, or optimization technique. The electrical impedance-based, 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, this technique is very sensitive to minor structural changes in the near field of the piezoelectric sensors, and therefore, it is able to detect the damage in its early stage. Optimization approaches must be used for the case where a good condensed model is known, while neural network can be also used to estimate the nature of damage without prior knowledge of the model of the structure. The paper concludes with an experimental example in a welded cubic aluminum structure, in order to verify the performance of these two proposed methodologies. |
publishDate |
2002 |
dc.date.none.fl_str_mv |
2002-01-01 2014-05-20T15:28:10Z 2014-05-20T15:28:10Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
Proceedings of Imac-xx: Structural Dynamics Vols I and Ii. Bethel: Soc Experimental Mechanics Inc., v. 4753, p. 484-490, 2002. 0277-786X http://hdl.handle.net/11449/38038 WOS:000176646000070 |
identifier_str_mv |
Proceedings of Imac-xx: Structural Dynamics Vols I and Ii. Bethel: Soc Experimental Mechanics Inc., v. 4753, p. 484-490, 2002. 0277-786X WOS:000176646000070 |
url |
http://hdl.handle.net/11449/38038 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Proceedings of Imac-xx: Structural Dynamics Vols I and Ii |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
484-490 |
dc.publisher.none.fl_str_mv |
Soc Experimental Mechanics Inc |
publisher.none.fl_str_mv |
Soc Experimental Mechanics Inc |
dc.source.none.fl_str_mv |
Web of Science 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_ |
1808129291230117888 |