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dc.contributor.authorFernandez-Navamuel, A. 
dc.contributor.authorMagalhães, Filipe
dc.contributor.authorZamora-Sánchez, Diego
dc.contributor.authorOmella, Ángel J.
dc.contributor.authorGarcia-Sanchez, David
dc.contributor.authorPardo, D. 
dc.date.accessioned2022-02-04T13:05:16Z
dc.date.available2022-02-04T13:05:16Z
dc.date.issued2022-01-01
dc.identifier.issn14759217
dc.identifier.urihttp://hdl.handle.net/20.500.11824/1423
dc.description.abstractThis paper proposes a Deep Learning Enhanced Principal Component Analysis (PCA) approach for outlier detection to assess the structural condition of bridges. We employ partially explainable autoencoder architecture to replicate and enhance the data compression and reconstruction ability of PCA. The particularity of the method lies in the addition of residual connections to account for nonlinearities. We apply the proposed method to monitoring data obtained from two bridges under real operation conditions and compare the results before and after adding the residual connections. Results show that the addition of residual connections enhances the outlier detection ability of the network, allowing to detect lighter damages.en_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectautoencoderen_US
dc.subjectDeep Learningen_US
dc.subjectPrincipal Component Analysisen_US
dc.subjectreconstruction erroren_US
dc.subjectStructural Health Monitoringen_US
dc.titleDeep learning enhanced principal component analysis for structural health monitoringen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.identifier.doi10.1177/14759217211041684en_US
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/777778en_US
dc.relation.projectIDES/1PE/SEV-2017-0718en_US
dc.relation.projectIDES/2PE/PID2019-108111RB-I00en_US
dc.relation.projectIDEUS/BERC/BERC.2018-2021en_US
dc.relation.projectIDEUS/ELKARTEKen_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersionen_US
dc.journal.titleStructural Health Monitoringen_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España