Supervised Deep Learning with Finite Element simulations for damage identification in bridges
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2022-04-15
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Abstract
This 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.
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This work has received funding from the European’s Union Horizon 2020 research and innovation program under the grant agreement No 769373 (FORESEE project). _x000D_ 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)._x000D_ 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.
Keywords
Structural Health Monitoring , Deep Learning , Damage identification , Autoencoders , Structural Health Monitoring , Deep Learning , Damage identification , Autoencoders , Civil and Structural Engineering , Project ID , info:eu-repo/grantAgreement/EC/H2020/769373/EU/Future proofing strategies FOr RESilient transport networks against Extreme Events/FORESEE , info:eu-repo/grantAgreement/EC/H2020/777778/EU/Multiscale Inversion of Porous Rock Physics using High-Performance Simulators: Bridging the Gap between Mathematics and Geophysics/MATHROCKS , info:eu-repo/grantAgreement/EC/H2020/769373/EU/Future proofing strategies FOr RESilient transport networks against Extreme Events/FORESEE , info:eu-repo/grantAgreement/EC/H2020/777778/EU/Multiscale Inversion of Porous Rock Physics using High-Performance Simulators: Bridging the Gap between Mathematics and Geophysics/MATHROCKS , Funding Info , This work has received funding from the European’s Union Horizon 2020 research and innovation program under the grant agreement No 769373 (FORESEE project). _x000D_ 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)._x000D_ 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 ( , This work has received funding from the European’s Union Horizon 2020 research and innovation program under the grant agreement No 769373 (FORESEE project). _x000D_ 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)._x000D_ 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 (
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Fernandez-Navamuel , A , Zamora-Sánchez , D , Omella , Á J , Pardo , D , Garcia-Sanchez , D & Magalhães , F 2022 , ' Supervised Deep Learning with Finite Element simulations for damage identification in bridges ' , Engineering Structures , vol. 257 , 114016 , pp. 114016 . https://doi.org/10.1016/j.engstruct.2022.114016