Bridge damage assessment under traffic and environmental variability: a case study on Yonghe cable-stayed bridge
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 environment...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/441622 |
| Acceso en línea: | https://hdl.handle.net/2117/441622 https://dx.doi.org/10.1016/j.engstruct.2025.120965 |
| Access Level: | acceso abierto |
| Palabra clave: | Bridge damage detection SHM Variational mode decomposition Hilbert-huang transform Principal component analysis K-means Àrees temàtiques de la UPC::Enginyeria civil::Materials i estructures::Tipologies estructurals |
| Sumario: | 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. |
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