An SHM approach using machine learning and statistical indicators extracted from raw dynamic measurements

Detalhes bibliográficos
Autor(a) principal: Finotti, Rafaelle Piazzaroli
Data de Publicação: 2019
Outros Autores: Cury, Alexandre Abrahão, Barbosa, Flávio de Souza
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFJF
Texto Completo: http://dx.doi.org/10.1590/1679-78254942
https://repositorio.ufjf.br/jspui/handle/ufjf/11191
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spelling 2019-10-24T11:41:20Z2019-05-212019-10-24T11:41:20Z2019-03-14162117http://dx.doi.org/10.1590/1679-78254942https://repositorio.ufjf.br/jspui/handle/ufjf/11191-Structural Health Monitoring using raw dynamic measurements is the subject of several studies aimed at identifying structural modifications or, more specifically, focused on damage assessment. Traditional damage detection methods associate structural modal deviations to damage. Nevertheless, the process used to determine modal characteristics can influence the results of such methods, which could lead to additional uncertainties. Thus, techniques combining machine learning and statistical analysis applied directly to raw measurements are being discussed in recent researches. The purpose of this paper is to investigate statistical indicators, little explored in damage identification methods, to characterize acceleration measurements directly in the time domain. Hence, the present work compares two machine learning algorithms to identify structural changes using statistics obtained from raw dynamic data. The algorithms are based on Artificial Neural Networks and Support Vector Machines. They are initially evaluated through numerical simulations using a simply supported beam model. Then, they are assessed through experimental tests performed on a laboratory beam structure and an actual railway bridge, in France. For all cases, different damage scenarios were considered. The obtained results encourage the development of computational tools using statistical indicators of acceleration measurements for structural alteration assessment.eng--BrasilLatin American Journal of Solids and Structures-Structural dynamicDamage identificationComputational intelligenceStructural health monitoringVibration monitoringDynamic measurementAn SHM approach using machine learning and statistical indicators extracted from raw dynamic measurementsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleFinotti, Rafaelle PiazzaroliCury, Alexandre AbrahãoBarbosa, Flávio de Souzainfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFJFinstname:Universidade Federal de Juiz de Fora (UFJF)instacron:UFJFTEXTAn SHM approach using machine learning and statistical.pdf.txtAn SHM approach using machine learning and statistical.pdf.txtExtracted texttext/plain51000https://repositorio.ufjf.br/jspui/bitstream/ufjf/11191/3/An%20SHM%20approach%20using%20machine%20learning%20and%20statistical.pdf.txt48dab8d3e1e7846cceff21f0c22036e9MD53THUMBNAILAn SHM approach using machine learning and statistical.pdf.jpgAn SHM approach using machine learning and statistical.pdf.jpgGenerated Thumbnailimage/jpeg1688https://repositorio.ufjf.br/jspui/bitstream/ufjf/11191/4/An%20SHM%20approach%20using%20machine%20learning%20and%20statistical.pdf.jpg98f0ac526de9cb66aaf3fab57043852fMD54ORIGINALAn SHM approach using machine learning and statistical.pdfAn SHM approach using machine learning and statistical.pdfapplication/pdf3200474https://repositorio.ufjf.br/jspui/bitstream/ufjf/11191/1/An%20SHM%20approach%20using%20machine%20learning%20and%20statistical.pdf31db35362332dabfc54df5435b14fbf1MD51LICENSElicense.txtlicense.txttext/plain; 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dc.title.pt_BR.fl_str_mv An SHM approach using machine learning and statistical indicators extracted from raw dynamic measurements
title An SHM approach using machine learning and statistical indicators extracted from raw dynamic measurements
spellingShingle An SHM approach using machine learning and statistical indicators extracted from raw dynamic measurements
Finotti, Rafaelle Piazzaroli
-
Structural dynamic
Damage identification
Computational intelligence
Structural health monitoring
Vibration monitoring
Dynamic measurement
title_short An SHM approach using machine learning and statistical indicators extracted from raw dynamic measurements
title_full An SHM approach using machine learning and statistical indicators extracted from raw dynamic measurements
title_fullStr An SHM approach using machine learning and statistical indicators extracted from raw dynamic measurements
title_full_unstemmed An SHM approach using machine learning and statistical indicators extracted from raw dynamic measurements
title_sort An SHM approach using machine learning and statistical indicators extracted from raw dynamic measurements
author Finotti, Rafaelle Piazzaroli
author_facet Finotti, Rafaelle Piazzaroli
Cury, Alexandre Abrahão
Barbosa, Flávio de Souza
author_role author
author2 Cury, Alexandre Abrahão
Barbosa, Flávio de Souza
author2_role author
author
dc.contributor.author.fl_str_mv Finotti, Rafaelle Piazzaroli
Cury, Alexandre Abrahão
Barbosa, Flávio de Souza
dc.subject.cnpq.fl_str_mv -
topic -
Structural dynamic
Damage identification
Computational intelligence
Structural health monitoring
Vibration monitoring
Dynamic measurement
dc.subject.por.fl_str_mv Structural dynamic
Damage identification
Computational intelligence
Structural health monitoring
Vibration monitoring
Dynamic measurement
description -
publishDate 2019
dc.date.accessioned.fl_str_mv 2019-10-24T11:41:20Z
dc.date.available.fl_str_mv 2019-05-21
2019-10-24T11:41:20Z
dc.date.issued.fl_str_mv 2019-03-14
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 https://repositorio.ufjf.br/jspui/handle/ufjf/11191
dc.identifier.doi.pt_BR.fl_str_mv http://dx.doi.org/10.1590/1679-78254942
url http://dx.doi.org/10.1590/1679-78254942
https://repositorio.ufjf.br/jspui/handle/ufjf/11191
dc.language.iso.fl_str_mv eng
language eng
dc.relation.ispartof.pt_BR.fl_str_mv Latin American Journal of Solids and Structures
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv -
dc.publisher.initials.fl_str_mv -
dc.publisher.country.fl_str_mv Brasil
publisher.none.fl_str_mv -
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFJF
instname:Universidade Federal de Juiz de Fora (UFJF)
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instname_str Universidade Federal de Juiz de Fora (UFJF)
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institution UFJF
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