Unsupervised autoencoders with features in the electromechanical impedance domain for early damage assessment in FRP-strengthened concrete elements

This paper presents the development of a robust automatic diagnosis technique that uses raw Electro-Mechanical Impedance (EMI) signals and deep autoencoder models to detect damage in fiber-reinforced-polymers (FRP) strengthened reinforced concrete (RC) elements, for which the most common failure mod...

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Detalles Bibliográficos
Autores: Perera, Ricardo, Montes, Javier, Gómez Arteta, Alejandra, Barris Peña, Cristina, Baena Muñoz, Marta
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10256/25951
Acceso en línea:http://hdl.handle.net/10256/25951
Access Level:acceso abierto
Palabra clave:Bigues de formigó
Concrete beams
Construcció en formigó armat amb fibres
Reinforced concrete construction
Resistència de materials
Strength of materials
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spelling Unsupervised autoencoders with features in the electromechanical impedance domain for early damage assessment in FRP-strengthened concrete elementsPerera, RicardoMontes, JavierGómez Arteta, AlejandraBarris Peña, CristinaBaena Muñoz, MartaBigues de formigóConcrete beamsConstrucció en formigó armat amb fibresReinforced concrete constructionResistència de materialsStrength of materialsThis paper presents the development of a robust automatic diagnosis technique that uses raw Electro-Mechanical Impedance (EMI) signals and deep autoencoder models to detect damage in fiber-reinforced-polymers (FRP) strengthened reinforced concrete (RC) elements, for which the most common failure modes occur in a sudden and brittle way by debonding. The contribution of this work is threefold: First, for the first time, two autoencoder models, convolutional and fully connected, based on an unsupervised learning framework supplemented by appropriate pre-processing techniques, are proposed for effective tracking of FRP-strengthened RC elements from raw EMI response variations in different locations of the auscultated structure; their implementation is also extensively investigated. The proposed framework consists of two main components, namely, dimensionality reduction and relationship learning. The first component is to reduce the dimensionality of the raw EMI signal while preserving the necessary information required, and the second component is to perform the relationship learning between the features with the reduced dimensionality and the stiffness reduction parameters of the structure. The approach is beneficial as only the EMI spectrum from the healthy structure state is considered for the training of the autoencoders. Second, the superior performance of the proposed framework is demonstrated. The results show that the proposed technique can accurately detect minor damage in its earliest stages for this kind of strengthened structures, while removing the need for manual or signal processing-based damage sensitive feature extraction from EMI signals for damage diagnosis. Finally, research presented in this work can potentially open up new opportunities for successful condition monitoring of this type of strengthened structuresThis research was funded by the Spanish Ministry of Science and Innovation (MCIN/AEI), grants number PID2020‐119015GB‐C21 and PID2020‐119015GB‐C22ElsevierAgencia Estatal de Investigación2024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionpeer-reviewedapplication/pdfhttp://hdl.handle.net/10256/25951http://hdl.handle.net/10256/25951Engineering Structures, 2024, vol. 315, art.núm.118458Articles publicats (D-EMCI)Perera, Ricardo Montes, Javier Gómez Arteta, Alejandra Barris Peña, Cristina Baena Muñoz, Marta 2024 Unsupervised autoencoders with features in the electromechanical impedance domain for early damage assessment in FRP-strengthened concrete elements Engineering Structures 315 art.núm.118458reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)Inglésinfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.engstruct.2024.118458info:eu-repo/semantics/altIdentifier/issn/0141-0296info:eu-repo/semantics/altIdentifier/eissn/1873-7323PID2020‐119015GB‐C22info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-119015GB-C22Reconeixement-NoComercial-SenseObraDerivada 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessoai:recercat.cat:10256/259512026-05-29T05:05:01Z
dc.title.none.fl_str_mv Unsupervised autoencoders with features in the electromechanical impedance domain for early damage assessment in FRP-strengthened concrete elements
title Unsupervised autoencoders with features in the electromechanical impedance domain for early damage assessment in FRP-strengthened concrete elements
spellingShingle Unsupervised autoencoders with features in the electromechanical impedance domain for early damage assessment in FRP-strengthened concrete elements
Perera, Ricardo
Bigues de formigó
Concrete beams
Construcció en formigó armat amb fibres
Reinforced concrete construction
Resistència de materials
Strength of materials
title_short Unsupervised autoencoders with features in the electromechanical impedance domain for early damage assessment in FRP-strengthened concrete elements
title_full Unsupervised autoencoders with features in the electromechanical impedance domain for early damage assessment in FRP-strengthened concrete elements
title_fullStr Unsupervised autoencoders with features in the electromechanical impedance domain for early damage assessment in FRP-strengthened concrete elements
title_full_unstemmed Unsupervised autoencoders with features in the electromechanical impedance domain for early damage assessment in FRP-strengthened concrete elements
title_sort Unsupervised autoencoders with features in the electromechanical impedance domain for early damage assessment in FRP-strengthened concrete elements
dc.creator.none.fl_str_mv Perera, Ricardo
Montes, Javier
Gómez Arteta, Alejandra
Barris Peña, Cristina
Baena Muñoz, Marta
author Perera, Ricardo
author_facet Perera, Ricardo
Montes, Javier
Gómez Arteta, Alejandra
Barris Peña, Cristina
Baena Muñoz, Marta
author_role author
author2 Montes, Javier
Gómez Arteta, Alejandra
Barris Peña, Cristina
Baena Muñoz, Marta
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Agencia Estatal de Investigación
dc.subject.none.fl_str_mv Bigues de formigó
Concrete beams
Construcció en formigó armat amb fibres
Reinforced concrete construction
Resistència de materials
Strength of materials
topic Bigues de formigó
Concrete beams
Construcció en formigó armat amb fibres
Reinforced concrete construction
Resistència de materials
Strength of materials
description This paper presents the development of a robust automatic diagnosis technique that uses raw Electro-Mechanical Impedance (EMI) signals and deep autoencoder models to detect damage in fiber-reinforced-polymers (FRP) strengthened reinforced concrete (RC) elements, for which the most common failure modes occur in a sudden and brittle way by debonding. The contribution of this work is threefold: First, for the first time, two autoencoder models, convolutional and fully connected, based on an unsupervised learning framework supplemented by appropriate pre-processing techniques, are proposed for effective tracking of FRP-strengthened RC elements from raw EMI response variations in different locations of the auscultated structure; their implementation is also extensively investigated. The proposed framework consists of two main components, namely, dimensionality reduction and relationship learning. The first component is to reduce the dimensionality of the raw EMI signal while preserving the necessary information required, and the second component is to perform the relationship learning between the features with the reduced dimensionality and the stiffness reduction parameters of the structure. The approach is beneficial as only the EMI spectrum from the healthy structure state is considered for the training of the autoencoders. Second, the superior performance of the proposed framework is demonstrated. The results show that the proposed technique can accurately detect minor damage in its earliest stages for this kind of strengthened structures, while removing the need for manual or signal processing-based damage sensitive feature extraction from EMI signals for damage diagnosis. Finally, research presented in this work can potentially open up new opportunities for successful condition monitoring of this type of strengthened structures
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
peer-reviewed
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10256/25951
http://hdl.handle.net/10256/25951
url http://hdl.handle.net/10256/25951
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.engstruct.2024.118458
info:eu-repo/semantics/altIdentifier/issn/0141-0296
info:eu-repo/semantics/altIdentifier/eissn/1873-7323
PID2020‐119015GB‐C22
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-119015GB-C22
dc.rights.none.fl_str_mv Reconeixement-NoComercial-SenseObraDerivada 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Reconeixement-NoComercial-SenseObraDerivada 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv Engineering Structures, 2024, vol. 315, art.núm.118458
Articles publicats (D-EMCI)
Perera, Ricardo Montes, Javier Gómez Arteta, Alejandra Barris Peña, Cristina Baena Muñoz, Marta 2024 Unsupervised autoencoders with features in the electromechanical impedance domain for early damage assessment in FRP-strengthened concrete elements Engineering Structures 315 art.núm.118458
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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