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dc.contributor.authorFernandez-Navamuel, A. 
dc.contributor.authorZamora-Sánchez, Diego
dc.contributor.authorOmella, Ángel J.
dc.contributor.authorPardo, D. 
dc.contributor.authorGarcia-Sanchez, David
dc.contributor.authorMagalhães, Filipe
dc.date.accessioned2022-04-25T18:32:08Z
dc.date.available2022-04-25T18:32:08Z
dc.date.issued2022-04-15
dc.identifier.issn01410296
dc.identifier.urihttp://hdl.handle.net/20.500.11824/1471
dc.description.abstractThis work proposes a supervised Deep Learning approach for damage identification in bridge structures. We employ a hybrid methodology that incorporates Finite Element simulations to enrich the training phase of a Deep Neural Network with synthetic damage scenarios. The neural network is based on autoencoders and its particular architecture allows to activate or deactivate nonlinear connections under need. The methodology intends to contribute to the progress towards the applicability of Structural Health Monitoring practices in full-scale bridge structures. The ultimate goal is to estimate the location and severity of damage from measurements of the dynamic response of the structure. The damages we seek to detect correspond to material degradations that affect wide areas of the structure by reducing its stiffness properties. Our method allows a feasible adaptation to large systems with complex parametrizations and structural particularities. We investigate the performance of the proposed method on two full-scale instrumented bridges, obtaining adequate results for the testing datasets even in presence of measurement uncertainty. Besides, the method successfully predicts the damage condition for two real damage scenarios of increasing severity available in one of the bridges.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.subjectAutoencodersen_US
dc.subjectDamage identificationen_US
dc.subjectDeep Learningen_US
dc.subjectStructural Health Monitoringen_US
dc.titleSupervised Deep Learning with Finite Element simulations for damage identification in bridgesen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.identifier.doi10.1016/j.engstruct.2022.114016en_US
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0141029622001638en_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/publishedVersionen_US
dc.journal.titleEngineering Structuresen_US
dc.volume.number257en_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