Assignment of structural behaviours in long-term monitoring : application to a strengthened railway bridge.
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFOP |
Texto Completo: | http://www.repositorio.ufop.br/handle/123456789/1530 http://journals.sagepub.com/doi/pdf/10.1177/1475921711434858 |
Resumo: | Novelty detection, the identification of data that is unusual or different, is relevant in a wide number of real-world scenarios, ranging from identifying unusual weather conditions to detecting evidence of damage in mechanical systems. Using novelty detection approaches for structural health monitoring presents significant challenges to the non-expert user. In this article, symbolic data analysis is introduced to model variability in tests. Hierarchy-divisive methods and dynamic clouds procedures are then used to discriminate structural changes used as novelty detection approaches for classifying structural behaviours. This article reports the study of experimental tests performed on a railway bridge in France. This bridge has undergone reinforcement works during the summer of 2003. Through the years of 2004–2006, new sets of dynamic tests were recorded. The main objective was to analyse the evolution of the bridge’s dynamic behaviour over time. To this end, the symbolic data analysis–based clustering methods are used for assigning new tests to clusters identified before and after strengthening or to highlight a totally different structural behaviour |
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Cury, Alexandre AbrahãoCrémona, Christian2012-10-03T18:09:37Z2012-10-03T18:09:37Z2012CURY, A. A.; CRÉMONA, C. Assignment of structural behaviours in long-term monitoring : application to a strengthened railway bridge. Structural Health Monitoring, v. 11, n. 4, p. 422-441, 2012. Disponível em: <http://journals.sagepub.com/doi/pdf/10.1177/1475921711434858>. Acesso em: 03 out. 201215452263http://www.repositorio.ufop.br/handle/123456789/1530http://journals.sagepub.com/doi/pdf/10.1177/1475921711434858Novelty detection, the identification of data that is unusual or different, is relevant in a wide number of real-world scenarios, ranging from identifying unusual weather conditions to detecting evidence of damage in mechanical systems. Using novelty detection approaches for structural health monitoring presents significant challenges to the non-expert user. In this article, symbolic data analysis is introduced to model variability in tests. Hierarchy-divisive methods and dynamic clouds procedures are then used to discriminate structural changes used as novelty detection approaches for classifying structural behaviours. This article reports the study of experimental tests performed on a railway bridge in France. This bridge has undergone reinforcement works during the summer of 2003. Through the years of 2004–2006, new sets of dynamic tests were recorded. The main objective was to analyse the evolution of the bridge’s dynamic behaviour over time. To this end, the symbolic data analysis–based clustering methods are used for assigning new tests to clusters identified before and after strengthening or to highlight a totally different structural behaviourNovelty detectionClusteringAssignmentSymbolic data analysisDynamic cloudsAssignment of structural behaviours in long-term monitoring : application to a strengthened railway bridge.info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleengreponame:Repositório Institucional da UFOPinstname:Universidade Federal de Ouro Preto (UFOP)instacron:UFOPinfo:eu-repo/semantics/openAccessLICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://www.repositorio.ufop.br/bitstream/123456789/1530/5/license.txt8a4605be74aa9ea9d79846c1fba20a33MD55ORIGINALARTIGO_AssignmentStructuralBehaviours.pdfARTIGO_AssignmentStructuralBehaviours.pdfapplication/pdf3178031http://www.repositorio.ufop.br/bitstream/123456789/1530/1/ARTIGO_AssignmentStructuralBehaviours.pdffed25c23d7690b0fda06e7930d159998MD51123456789/15302017-03-06 07:00:14.464oai:localhost: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Repositório InstitucionalPUBhttp://www.repositorio.ufop.br/oai/requestrepositorio@ufop.edu.bropendoar:32332017-03-06T12:00:14Repositório Institucional da UFOP - Universidade Federal de Ouro Preto (UFOP)false |
dc.title.pt_BR.fl_str_mv |
Assignment of structural behaviours in long-term monitoring : application to a strengthened railway bridge. |
title |
Assignment of structural behaviours in long-term monitoring : application to a strengthened railway bridge. |
spellingShingle |
Assignment of structural behaviours in long-term monitoring : application to a strengthened railway bridge. Cury, Alexandre Abrahão Novelty detection Clustering Assignment Symbolic data analysis Dynamic clouds |
title_short |
Assignment of structural behaviours in long-term monitoring : application to a strengthened railway bridge. |
title_full |
Assignment of structural behaviours in long-term monitoring : application to a strengthened railway bridge. |
title_fullStr |
Assignment of structural behaviours in long-term monitoring : application to a strengthened railway bridge. |
title_full_unstemmed |
Assignment of structural behaviours in long-term monitoring : application to a strengthened railway bridge. |
title_sort |
Assignment of structural behaviours in long-term monitoring : application to a strengthened railway bridge. |
author |
Cury, Alexandre Abrahão |
author_facet |
Cury, Alexandre Abrahão Crémona, Christian |
author_role |
author |
author2 |
Crémona, Christian |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Cury, Alexandre Abrahão Crémona, Christian |
dc.subject.por.fl_str_mv |
Novelty detection Clustering Assignment Symbolic data analysis Dynamic clouds |
topic |
Novelty detection Clustering Assignment Symbolic data analysis Dynamic clouds |
description |
Novelty detection, the identification of data that is unusual or different, is relevant in a wide number of real-world scenarios, ranging from identifying unusual weather conditions to detecting evidence of damage in mechanical systems. Using novelty detection approaches for structural health monitoring presents significant challenges to the non-expert user. In this article, symbolic data analysis is introduced to model variability in tests. Hierarchy-divisive methods and dynamic clouds procedures are then used to discriminate structural changes used as novelty detection approaches for classifying structural behaviours. This article reports the study of experimental tests performed on a railway bridge in France. This bridge has undergone reinforcement works during the summer of 2003. Through the years of 2004–2006, new sets of dynamic tests were recorded. The main objective was to analyse the evolution of the bridge’s dynamic behaviour over time. To this end, the symbolic data analysis–based clustering methods are used for assigning new tests to clusters identified before and after strengthening or to highlight a totally different structural behaviour |
publishDate |
2012 |
dc.date.accessioned.fl_str_mv |
2012-10-03T18:09:37Z |
dc.date.available.fl_str_mv |
2012-10-03T18:09:37Z |
dc.date.issued.fl_str_mv |
2012 |
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.citation.fl_str_mv |
CURY, A. A.; CRÉMONA, C. Assignment of structural behaviours in long-term monitoring : application to a strengthened railway bridge. Structural Health Monitoring, v. 11, n. 4, p. 422-441, 2012. Disponível em: <http://journals.sagepub.com/doi/pdf/10.1177/1475921711434858>. Acesso em: 03 out. 2012 |
dc.identifier.uri.fl_str_mv |
http://www.repositorio.ufop.br/handle/123456789/1530 |
dc.identifier.issn.none.fl_str_mv |
15452263 |
dc.identifier.uri2.none.fl_str_mv |
http://journals.sagepub.com/doi/pdf/10.1177/1475921711434858 |
identifier_str_mv |
CURY, A. A.; CRÉMONA, C. Assignment of structural behaviours in long-term monitoring : application to a strengthened railway bridge. Structural Health Monitoring, v. 11, n. 4, p. 422-441, 2012. Disponível em: <http://journals.sagepub.com/doi/pdf/10.1177/1475921711434858>. Acesso em: 03 out. 2012 15452263 |
url |
http://www.repositorio.ufop.br/handle/123456789/1530 http://journals.sagepub.com/doi/pdf/10.1177/1475921711434858 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
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info:eu-repo/semantics/openAccess |
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openAccess |
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reponame:Repositório Institucional da UFOP instname:Universidade Federal de Ouro Preto (UFOP) instacron:UFOP |
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