Applicability of the Statistical Pattern Recognition Paradigm for Structural Health Monitoring of Bridges
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.14/15656 |
Resumo: | In the last decades, health monitoring systems have gained an increasing importance in our society. The main purpose of these systems is to support the engineers to get more insight into the behavior of structures under service conditions, so they can optimize and improve maintenance programs and, hopefully, to avoid structural failures or disasters. It is possible to integrate these systems in any type of civil or mechanical infrastructure. However, in this dissertation, the preferential targets are the civil infrastructures with major strategically importance in the social environment, such as bridges and viaducts. Therefore, the goal of this dissertation is (i) to review the most recent bridge collapses in order to unveil the main causes and challenges posed by those catastrophic events; (ii) to review the concept and need of Structural Health Monitoring (SHM) of bridges as well as its associated potential for significant life-safety and economic benefits; and (iii) to study the applicability of the SHM concepts. Due to recent promising research developments, the SHM process is posed in the context of the Statistical Pattern Recognition (SPR) paradigm, which tries to implement a damage identification strategy based on the comparison of different state conditions. The applicability of the SHM-SPR paradigm is studied by applying its concepts in two separate cases: firstly on data sets from a base-excited three-story frame structure, created and tested in a laboratory environment at Los Alamos National Laboratory; secondly, on data sets from a real-world bridge, namely the Z24 Bridge in Switzerland. The major contributions of this dissertation are the extension of previous results obtained by Figueiredo et al. from the three-story frame structure and the development and application of an algorithm that uses a Gaussian mixture model as a way of improving the feature classification performance under varying operational and environmental conditions. |
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Applicability of the Statistical Pattern Recognition Paradigm for Structural Health Monitoring of BridgesDamage detectionBridge failuresStatistical Pattern Recognition ParadigmStructural health monitoringDeteção de danoDesastres de pontesParadigma de Reconhecimento de PadrõesMonitorização da integridade estruturalDomínio/Área Científica::Engenharia e Tecnologia::Engenharia CivilIn the last decades, health monitoring systems have gained an increasing importance in our society. The main purpose of these systems is to support the engineers to get more insight into the behavior of structures under service conditions, so they can optimize and improve maintenance programs and, hopefully, to avoid structural failures or disasters. It is possible to integrate these systems in any type of civil or mechanical infrastructure. However, in this dissertation, the preferential targets are the civil infrastructures with major strategically importance in the social environment, such as bridges and viaducts. Therefore, the goal of this dissertation is (i) to review the most recent bridge collapses in order to unveil the main causes and challenges posed by those catastrophic events; (ii) to review the concept and need of Structural Health Monitoring (SHM) of bridges as well as its associated potential for significant life-safety and economic benefits; and (iii) to study the applicability of the SHM concepts. Due to recent promising research developments, the SHM process is posed in the context of the Statistical Pattern Recognition (SPR) paradigm, which tries to implement a damage identification strategy based on the comparison of different state conditions. The applicability of the SHM-SPR paradigm is studied by applying its concepts in two separate cases: firstly on data sets from a base-excited three-story frame structure, created and tested in a laboratory environment at Los Alamos National Laboratory; secondly, on data sets from a real-world bridge, namely the Z24 Bridge in Switzerland. The major contributions of this dissertation are the extension of previous results obtained by Figueiredo et al. from the three-story frame structure and the development and application of an algorithm that uses a Gaussian mixture model as a way of improving the feature classification performance under varying operational and environmental conditions.Nas últimas décadas, os sistemas de monitorização estrutural ganharam uma crescente importância na nossa sociedade. O principal objetivo destes sistemas é de ajudar os engenheiros a aprofundar o conhecimento relativo ao comportamento das estruturas sob condições de serviço para que possam otimizar e melhorar os programas de manutenção e, em último caso, evitar desastres ou falhas estruturais. É possível integrar estes sistemas em qualquer tipo de infra-estrutura civil ou mecânica. No entanto, nesta dissertação, os alvos preferenciais são as infra-estruturas com elevada importância estratégica no seio da engenharia civil, tais como as pontes e os viadutos. Portanto, o objetivo desta dissertação é (i) rever os recentes colapsos de pontes, de forma a desvendar as causas que os originaram assim como os desafios colocados por estes eventos; (ii) rever o conceito e a necessidade de sistemas de monitorização da integridade estrutural (SHM) de pontes, bem como o seu potencial associado aos benefícios ao nível da segurança e do ponto de vista económico; e (iii) estudar a aplicabilidade dos conceitos da SHM. Devido a recentes desenvolvimentos promissores, o processo de SHM pode ser colocado no contexto de um paradigma de reconhecimento de padrões (SPR), o qual tenta implementar uma estratégia de identificação de dano com base na comparação de diferentes estados de condição da estrutura. A aplicabilidade do paradigma SHM-SPR é estudada através da aplicação dos seus conceitos em dois casos distintos: em primeiro lugar, em conjuntos de dados recolhidos de uma estrutura de três pisos, criada e testada em ambiente laboratorial no Los Alamos National Laboratory; em segundo lugar, em conjuntos de dados de uma ponte real, mais especificamente, a Ponte Z24, na Suíça. As contribuições originais desta dissertação são a extensão dos resultados anteriormente obtidos por Figueiredo et al. relativos à estrutura de três pisos, e o desenvolvimento e aplicação de um algoritmo, que utiliza como base um modelo de mistura Gaussiana, de forma a melhorar o desempenho da classificação de características sob condições operacionais e ambientais variáveis.Figueiredo, Elói João FariaVeritati - Repositório Institucional da Universidade Católica PortuguesaPereira, João Tiago Martins Neves2014-11-14T13:17:21Z2012-09-2120122012-09-21T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10400.14/15656enginfo: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:RCAAP2023-09-12T01:37:27Zoai:repositorio.ucp.pt:10400.14/15656Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:13:01.736896Repositório 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 |
Applicability of the Statistical Pattern Recognition Paradigm for Structural Health Monitoring of Bridges |
title |
Applicability of the Statistical Pattern Recognition Paradigm for Structural Health Monitoring of Bridges |
spellingShingle |
Applicability of the Statistical Pattern Recognition Paradigm for Structural Health Monitoring of Bridges Pereira, João Tiago Martins Neves Damage detection Bridge failures Statistical Pattern Recognition Paradigm Structural health monitoring Deteção de dano Desastres de pontes Paradigma de Reconhecimento de Padrões Monitorização da integridade estrutural Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Civil |
title_short |
Applicability of the Statistical Pattern Recognition Paradigm for Structural Health Monitoring of Bridges |
title_full |
Applicability of the Statistical Pattern Recognition Paradigm for Structural Health Monitoring of Bridges |
title_fullStr |
Applicability of the Statistical Pattern Recognition Paradigm for Structural Health Monitoring of Bridges |
title_full_unstemmed |
Applicability of the Statistical Pattern Recognition Paradigm for Structural Health Monitoring of Bridges |
title_sort |
Applicability of the Statistical Pattern Recognition Paradigm for Structural Health Monitoring of Bridges |
author |
Pereira, João Tiago Martins Neves |
author_facet |
Pereira, João Tiago Martins Neves |
author_role |
author |
dc.contributor.none.fl_str_mv |
Figueiredo, Elói João Faria Veritati - Repositório Institucional da Universidade Católica Portuguesa |
dc.contributor.author.fl_str_mv |
Pereira, João Tiago Martins Neves |
dc.subject.por.fl_str_mv |
Damage detection Bridge failures Statistical Pattern Recognition Paradigm Structural health monitoring Deteção de dano Desastres de pontes Paradigma de Reconhecimento de Padrões Monitorização da integridade estrutural Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Civil |
topic |
Damage detection Bridge failures Statistical Pattern Recognition Paradigm Structural health monitoring Deteção de dano Desastres de pontes Paradigma de Reconhecimento de Padrões Monitorização da integridade estrutural Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Civil |
description |
In the last decades, health monitoring systems have gained an increasing importance in our society. The main purpose of these systems is to support the engineers to get more insight into the behavior of structures under service conditions, so they can optimize and improve maintenance programs and, hopefully, to avoid structural failures or disasters. It is possible to integrate these systems in any type of civil or mechanical infrastructure. However, in this dissertation, the preferential targets are the civil infrastructures with major strategically importance in the social environment, such as bridges and viaducts. Therefore, the goal of this dissertation is (i) to review the most recent bridge collapses in order to unveil the main causes and challenges posed by those catastrophic events; (ii) to review the concept and need of Structural Health Monitoring (SHM) of bridges as well as its associated potential for significant life-safety and economic benefits; and (iii) to study the applicability of the SHM concepts. Due to recent promising research developments, the SHM process is posed in the context of the Statistical Pattern Recognition (SPR) paradigm, which tries to implement a damage identification strategy based on the comparison of different state conditions. The applicability of the SHM-SPR paradigm is studied by applying its concepts in two separate cases: firstly on data sets from a base-excited three-story frame structure, created and tested in a laboratory environment at Los Alamos National Laboratory; secondly, on data sets from a real-world bridge, namely the Z24 Bridge in Switzerland. The major contributions of this dissertation are the extension of previous results obtained by Figueiredo et al. from the three-story frame structure and the development and application of an algorithm that uses a Gaussian mixture model as a way of improving the feature classification performance under varying operational and environmental conditions. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012-09-21 2012 2012-09-21T00:00:00Z 2014-11-14T13:17:21Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/masterThesis |
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masterThesis |
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publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.14/15656 |
url |
http://hdl.handle.net/10400.14/15656 |
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eng |
language |
eng |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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