2026-04-14T07:57:50Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/4416222026-01-21T04:52:58Zcom_2072_1033col_2072_452950

Bridge damage assessment under traffic and environmental variability: a case study on Yonghe cable-stayed bridge Zunino, Leonardo Casas Rius, Joan Ramon Domaneschi, Marco Universitat Politècnica de Catalunya. Departament d'Enginyeria Civil i Ambiental Universitat Politècnica de Catalunya. EC - Enginyeria de la Construcció Àrees temàtiques de la UPC::Enginyeria civil::Materials i estructures::Tipologies estructurals Bridge damage detection SHM Variational mode decomposition Hilbert-huang transform Principal component analysis K-means Bridges are essential components of civil infrastructure that must operate safely and reliably. Traditional methods for assessing structural health rely on the concept that changes in a structure’s dynamic response may indicate potential damage. However, variations due to operational and environmental factors (like traffic and temperature) can also contribute to these changes. This makes damage detection more challenging, as a bridge may still be safe while exhibiting changes in its dynamic response due to these factors. If these effects are not properly accounted for, it could lead to false positive alerts. This article proposes a methodology for detecting and localizing damage in bridges subjected to traffic loads and environmental variability. Acceleration signals from accelerometers placed on the deck of a cable-stayed bridge in China were analyzed as part of a real monitoring effort. This data bank enabled the implementation of the algorithm on real signals in both undamaged and damaged scenarios. Variational Mode Decomposition is used to decompose the signal into Intrinsic Mode Functions. The Hilbert Transform is then employed to extract instantaneous frequencies, which represent damage-sensitive features in this context. Furthermore, environmental effects are removed from the damage-sensitive features using Principal Component Analysis. Finally, damage detection and localization are achieved using a statistical analysis able to confirm the previous data processing. An unsupervised clustering algorithm (K-means) is used to detect changes between the undamaged state and the damaged one. The results demonstrate the method’s effectiveness when applied to real-world scenarios, suggesting its potential application in structural health monitoring. The authors would like to express their sincere gratitude to Prof. Hui Li, Harbin Institute of Technology, for providing the data and images of the actual damage conditions of the Yonghe Bridge. Funding: This work was supported by MIUR - PRIN 2022 ‘‘BIORESTORE – BIO-based Resilient Energy and Seismic retrofiT Of the REsidential building stock’’ [Prot. 202234HM8J, CUP E53C24002680006]; and by MCIN/AEI - ‘‘ERDF A way of making Europe’’ [Grant PID2021- 126405OB-C31]. This publication is also part of the project PNRRNGEU which has received funding from the MUR – DM 629/2024. Peer Reviewed Postprint (published version) 2025-11 Article Zunino, L.; Casas, J.; Domaneschi, M. Bridge damage assessment under traffic and environmental variability: a case study on Yonghe cable-stayed bridge. «Engineering structures», Novembre 2025, vol. 343, Part A, article 120965. 1873-7323 https://hdl.handle.net/2117/441622 10.1016/j.engstruct.2025.120965 eng https://www.sciencedirect.com/science/article/pii/S0141029625013562 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-126405OB-C31/ES/DESARROLLO DE SENSORES MODULARES DE BAJO COSTE PARA SU USO EN IDENTIFICACION ESTRUCTURAL DE PUENTES SOMETIDOS A CARGAS QUASIESTATICAS/ http://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access Attribution-NonCommercial-NoDerivatives 4.0 International 16 p. text/html application/pdf Elsevier