Damage Detection Approach for Bridges under Temperature Effects using Gaussian Process Regression Trained with Hybrid Data

Detalhes bibliográficos
Autor(a) principal: Silva, Samuel da [UNESP]
Data de Publicação: 2022
Outros Autores: Figueiredo, Eloi, Moldovan, Ionut
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1061/(ASCE)BE.1943-5592.0001949
http://hdl.handle.net/11449/237866
Resumo: The success of detecting damage robustly relies on the availability of long periods of past data covering multiple weather scenarios and on the information contained in the data used during the learning process. Thus, the innovation of this paper is to apply a hybrid data set to train a Gaussian process regression, assuming a practically plausible range of environmental conditions. The proposed model presents a satisfactory performance to detect damage when structural changes caused by damage are blurred with changes caused by temperature. Rather than relying exclusively on experimental data, this strategy use finite-element models to generate complementary data when the structure is undamaged under a broad spectrum of temperature variations that are not measured. Once the stochastic interpolation is defined, the damage detection model is tested using experimental data considering different damage levels and temperature conditions. Induced settlements of a bridge pier are used as realistic damage scenarios. The Z24 prestressed concrete highway bridge in Switzerland is used to demonstrate the applicability of the proposed strategy. (c) 2022 American Society of Civil Engineers.
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spelling Damage Detection Approach for Bridges under Temperature Effects using Gaussian Process Regression Trained with Hybrid DataThe success of detecting damage robustly relies on the availability of long periods of past data covering multiple weather scenarios and on the information contained in the data used during the learning process. Thus, the innovation of this paper is to apply a hybrid data set to train a Gaussian process regression, assuming a practically plausible range of environmental conditions. The proposed model presents a satisfactory performance to detect damage when structural changes caused by damage are blurred with changes caused by temperature. Rather than relying exclusively on experimental data, this strategy use finite-element models to generate complementary data when the structure is undamaged under a broad spectrum of temperature variations that are not measured. Once the stochastic interpolation is defined, the damage detection model is tested using experimental data considering different damage levels and temperature conditions. Induced settlements of a bridge pier are used as realistic damage scenarios. The Z24 prestressed concrete highway bridge in Switzerland is used to demonstrate the applicability of the proposed strategy. (c) 2022 American Society of Civil Engineers.KU Leuven (Belgium) Structural Mechanics Section as the Z24 Bridge data sourceCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Portuguese National Funding Agency for Science Research and Technology (FCT/Portugal)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)UNESP Sao Paulo State Univ, Dept Mech Engn, Av Brasil 56, BR-15385000 Ilha Solteira, SP, BrazilLusofona Univ, Fac Engn, Campo Grande 376, P-1749024 Lisbon, PortugalUniv Lisbon, CERIS, Inst Super Tecn, Av Rovisco Pais 1, P-1049001 Lisbon, PortugalUNESP Sao Paulo State Univ, Dept Mech Engn, Av Brasil 56, BR-15385000 Ilha Solteira, SP, BrazilCAPES: 001CNPq: 306526/2019-0FAPESP: 19/19684-3Portuguese National Funding Agency for Science Research and Technology (FCT/Portugal): UIDB/04625/2020Asce-amer Soc Civil EngineersUniversidade Estadual Paulista (UNESP)Lusofona UnivUniv LisbonSilva, Samuel da [UNESP]Figueiredo, EloiMoldovan, Ionut2022-11-30T13:47:05Z2022-11-30T13:47:05Z2022-11-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article12http://dx.doi.org/10.1061/(ASCE)BE.1943-5592.0001949Journal Of Bridge Engineering. Reston: Asce-amer Soc Civil Engineers, v. 27, n. 11, 12 p., 2022.1084-0702http://hdl.handle.net/11449/23786610.1061/(ASCE)BE.1943-5592.0001949WOS:000853871600009Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal Of Bridge Engineeringinfo:eu-repo/semantics/openAccess2024-07-04T20:06:06Zoai:repositorio.unesp.br:11449/237866Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:25:26.902154Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Damage Detection Approach for Bridges under Temperature Effects using Gaussian Process Regression Trained with Hybrid Data
title Damage Detection Approach for Bridges under Temperature Effects using Gaussian Process Regression Trained with Hybrid Data
spellingShingle Damage Detection Approach for Bridges under Temperature Effects using Gaussian Process Regression Trained with Hybrid Data
Silva, Samuel da [UNESP]
title_short Damage Detection Approach for Bridges under Temperature Effects using Gaussian Process Regression Trained with Hybrid Data
title_full Damage Detection Approach for Bridges under Temperature Effects using Gaussian Process Regression Trained with Hybrid Data
title_fullStr Damage Detection Approach for Bridges under Temperature Effects using Gaussian Process Regression Trained with Hybrid Data
title_full_unstemmed Damage Detection Approach for Bridges under Temperature Effects using Gaussian Process Regression Trained with Hybrid Data
title_sort Damage Detection Approach for Bridges under Temperature Effects using Gaussian Process Regression Trained with Hybrid Data
author Silva, Samuel da [UNESP]
author_facet Silva, Samuel da [UNESP]
Figueiredo, Eloi
Moldovan, Ionut
author_role author
author2 Figueiredo, Eloi
Moldovan, Ionut
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
Lusofona Univ
Univ Lisbon
dc.contributor.author.fl_str_mv Silva, Samuel da [UNESP]
Figueiredo, Eloi
Moldovan, Ionut
description The success of detecting damage robustly relies on the availability of long periods of past data covering multiple weather scenarios and on the information contained in the data used during the learning process. Thus, the innovation of this paper is to apply a hybrid data set to train a Gaussian process regression, assuming a practically plausible range of environmental conditions. The proposed model presents a satisfactory performance to detect damage when structural changes caused by damage are blurred with changes caused by temperature. Rather than relying exclusively on experimental data, this strategy use finite-element models to generate complementary data when the structure is undamaged under a broad spectrum of temperature variations that are not measured. Once the stochastic interpolation is defined, the damage detection model is tested using experimental data considering different damage levels and temperature conditions. Induced settlements of a bridge pier are used as realistic damage scenarios. The Z24 prestressed concrete highway bridge in Switzerland is used to demonstrate the applicability of the proposed strategy. (c) 2022 American Society of Civil Engineers.
publishDate 2022
dc.date.none.fl_str_mv 2022-11-30T13:47:05Z
2022-11-30T13:47:05Z
2022-11-01
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 http://dx.doi.org/10.1061/(ASCE)BE.1943-5592.0001949
Journal Of Bridge Engineering. Reston: Asce-amer Soc Civil Engineers, v. 27, n. 11, 12 p., 2022.
1084-0702
http://hdl.handle.net/11449/237866
10.1061/(ASCE)BE.1943-5592.0001949
WOS:000853871600009
url http://dx.doi.org/10.1061/(ASCE)BE.1943-5592.0001949
http://hdl.handle.net/11449/237866
identifier_str_mv Journal Of Bridge Engineering. Reston: Asce-amer Soc Civil Engineers, v. 27, n. 11, 12 p., 2022.
1084-0702
10.1061/(ASCE)BE.1943-5592.0001949
WOS:000853871600009
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Journal Of Bridge Engineering
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 12
dc.publisher.none.fl_str_mv Asce-amer Soc Civil Engineers
publisher.none.fl_str_mv Asce-amer Soc Civil Engineers
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
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