Bridge damage detection based on vibration data: past and new developments
Overtime, bridge condition declines due to a number of degradation processes such as creep, corrosion, and cyclic loading, among others. Traditionally, vibration-based damage detection techniques in bridges have focused on monitoring changes to modal parameters. These techniques can often suffer to...
| Autores: | , |
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| Tipo de documento: | artigo |
| Data de publicação: | 2017 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositório: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglês |
| OAI Identifier: | oai:upcommons.upc.edu:2117/100561 |
| Acesso em linha: | https://hdl.handle.net/2117/100561 https://dx.doi.org/10.3389/fbuil.2017.00004 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Bridges--Vibration structural health monitoring damage detection vibration modal parameters bridges Ponts -- Vibració Àrees temàtiques de la UPC::Enginyeria civil::Materials i estructures::Tipologies estructurals |
| Resumo: | Overtime, bridge condition declines due to a number of degradation processes such as creep, corrosion, and cyclic loading, among others. Traditionally, vibration-based damage detection techniques in bridges have focused on monitoring changes to modal parameters. These techniques can often suffer to their sensitivity to changes in environmental and operational conditions, mistaking them as structural damage. Recent research has seen the emergence of more advanced computational techniques that not only allow the assessment of noisier and more complex data but also allow research to veer away from monitoring changes in modal parameters alone. This paper presents a review of the current state-of-the-art developments in vibration-based damage detection in small to medium span bridges with particular focus on the utilization of advanced computational methods that avoid traditional damage detection pitfalls. A case study based on the S101 bridge is also presented to test the damage sensitivity to a chosen methodology. |
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