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dc.contributor.authorFernandez-Navamuel, Ana
dc.contributor.authorZamora-Sánchez, Diego
dc.contributor.authorOmella, Ángel J.
dc.contributor.authorPardo, David
dc.contributor.authorGarcia-Sanchez, David
dc.contributor.authorMagalhães, Filipe
dc.date.accessioned2022-03-08T11:36:19Z
dc.date.available2022-03-08T11:36:19Z
dc.date.issued2022-04-15
dc.identifier.citationFernandez-Navamuel, Ana, Diego Zamora-Sánchez, Ángel J. Omella, David Pardo, David Garcia-Sanchez, and Filipe Magalhães. “Supervised Deep Learning with Finite Element Simulations for Damage Identification in Bridges.” Engineering Structures 257 (April 2022): 114016. doi:10.1016/j.engstruct.2022.114016.en
dc.identifier.issn0141-0296en
dc.identifier.urihttp://hdl.handle.net/11556/1278
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
dc.description.sponsorshipThis work has received funding from the European’s Union Horizon 2020 research and innovation program under the grant agreement No 769373 (FORESEE project). Authors would like to acknowledge the Basque Government funding within the ELKARTEK programme (SIGZE project (KK-2021/00095)). This work was financially supported by: Base Funding - UIDB/04708/ 2020 of the CONSTRUCT - Instituto de I&D em Estruturas e Construções - funded by national funds through the FCT/MCTES (PIDDAC). David Pardo has received funding from: the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 777778 (MATHROCKS); the European Regional Development Fund (ERDF) through the Interreg V-A Spain-France-Andorra program POCTEFA 2014-2020 Project PIXIL (EFA362/19); the Spanish Ministry of Science and Innovation projects with references PID2019-108111RB-I00 (FEDER/AEI) and PDC2021-121093-I00, the ‘‘BCAM Severo Ochoa’’ accreditation of excellence (SEV-2017-0718); and the Basque Government through the BERC 2018-2021 program, the three Elkartek projects 3KIA (KK-2020/00049), EXPERTIA (KK-2021/00048), and SIGZE (KK-2021/00095), and the Consolidated Research Group MATHMODE (IT1294-19) given by the Department of Education.en
dc.language.isoengen
dc.publisherElsevieren
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleSupervised Deep Learning with Finite Element simulations for damage identification in bridgesen
dc.typejournal articleen
dc.identifier.doi10.1016/j.engstruct.2022.114016en
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/769373/EU/Future proofing strategies FOr RESilient transport networks against Extreme Events/FORESEEen
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/777778/EU/Multiscale Inversion of Porous Rock Physics using High-Performance Simulators: Bridging the Gap between Mathematics and Geophysics/MATHROCKSen
dc.rights.accessRightsopen accessen
dc.subject.keywordsStructural Health Monitoringen
dc.subject.keywordsDeep Learningen
dc.subject.keywordsDamage identificationen
dc.subject.keywordsAutoencodersen
dc.identifier.essn1873-7323en
dc.journal.titleEngineering Structuresen
dc.page.initial114016en
dc.volume.number257en


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