Damage Detection Approach for Bridges under Temperature Effects using Gaussian Process Regression Trained with Hybrid Data
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , |
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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Repositório Institucional da UNESP |
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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 |
|
_version_ |
1808128809003646976 |