RT Journal Article
T1 Bridge damage identification under varying environmental and operational conditions combining Deep Learning and numerical simulations
A1 Fernandez-Navamuel, Ana
A1 Pardo, David
A1 Magalhães, Filipe
A1 Zamora-Sánchez, Diego
A1 Omella, Ángel J.
A1 Garcia-Sanchez, David
AB This work proposes a novel supervised learning approach to identify damage in operating bridge structures. We propose a method to introduce the effect of environmental and operational conditions into the synthetic damage scenarios employed for training a Deep Neural Network, which is applicable to large-scale complex structures. We apply a clustering technique based on Gaussian Mixtures to effectively select Q representative measurements from a long-term monitoring dataset. We employ these measurements as the target response to solve various Finite Element Model Updating problems before generating different damage scenarios. The synthetic and experimental measurements feed two Deep Neural Networks that assess the structural health condition in terms of damage severity and location. We demonstrate the applicability of the proposed method with a real full-scale case study: the Infante Dom Henrique bridge in Porto. A comparative study reveals that neglecting different environmental and operational conditions during training detracts the damage identification task. By contrast, our method provides successful results during a synthetic validation.
SN 0888-3270
YR 2023
FD 2023-10-01
LA eng
NO Fernandez-Navamuel , A , Pardo , D , Magalhães , F , Zamora-Sánchez , D , Omella , Á J & Garcia-Sanchez , D 2023 , ' Bridge damage identification under varying environmental and operational conditions combining Deep Learning and numerical simulations ' , Mechanical Systems and Signal Processing , vol. 200 , 110471 . https://doi.org/10.1016/j.ymssp.2023.110471
NO Publisher Copyright: © 2023 The Authors
NO David Pardo has received funding from: the Spanish Ministry of Science and Innovation projects with references TED2021-132783B-I00 , PID2019-108111RB-I00 (FEDER/AEI) and PDC2021-121093-I00 (MCIN/AEI/10.13039/501100011033/Next Generation EU), the “ BCAM Severo Ochoa ” accreditation of excellence CEX2021-001142-S/MICIN/AEI/10.13039/501100011033 ; the Spanish Ministry of Economic and Digital Transformation with Misiones Project IA4TES ( MIA.2021.M04.008 /NextGenerationEU PRTR); and the Basque Government through the BERC 2022-2025 program , the Elkartek project SIGZE ( KK-2021/00095 ), and the Consolidated Research Group MATHMODE ( IT1456-22 ) given by the Department of Education. 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). Authors would like to acknowledge the Basque Government funding within the ELKARTEK programme (SIGZE project ( KK-2021/00095 )), the European Horizon (HE) with LIASON project ( GA 101103698 ), and FUTURAL project .
DS TECNALIA Publications
RD 15 sept 2024