Key findings from long-term operational modal analysis of a landmark steel arch bridge in Italy
Metadatos
Mostrar el registro completo del ítemAutor
Tomassini, Elisa; Centofanti, Gianluca; Chellini, Giuseppe; García-Macías, Enrique; Lepori, Lorenzo; Mannella, Paolo; Salvatore, Walter; Ubertini, FilippoEditorial
Elsevier
Materia
Arch bridge Bridges Continuous monitoring
Fecha
2025-12Referencia bibliográfica
Tomassini, E., Centofanti, G., Chellini, G., García-Macías, E., Lepori, L., Mannella, P., Salvatore, W., & Ubertini, F. (2025). Key findings from long-term operational modal analysis of a landmark steel arch bridge in Italy. Structures, 82(110436), 110436. https://doi.org/10.1016/j.istruc.2025.110436
Resumen
The integrity of bridge infrastructure is crucial for public safety and the reliable operation of transportation
networks. Many aging bridges, designed under outdated standards, demand advanced monitoring to ensure continued performance. Structural Health Monitoring (SHM) systems, particularly vibration-based techniques such
as Operational Modal Analysis (OMA), provide non-invasive tools to estimate dynamic parameters which serve
as indicators of potential structural degradation. Beyond improving safety, SHM fosters collaboration between
researchers and infrastructure managers, deepening the understanding of bridge dynamics under environmental
and operational conditions and strengthening strategies for safer transportation networks. This study presents
the application of SHM methodologies to the Marmore Bridge, a landmark long-span steel arch bridge monitored
over seven months, offering insights into its structural behavior and long-term performance. A detailed analysis
of the bridge was performed by integrating design documents, structural models, previous OMA studies, and the
current automated SHM framework. Particular emphasis was placed on assessing different statistical models to
evaluate their suitability in characterizing the effects of environmental conditions, and the derived control charts
for damage detection were tested during a 25-day assessment period. Long-term frequency variations exhibited
a nonlinear correlation with temperature and a weaker linear correlation with RMS accelerations, highlighting
the influence of operational loads on the bridge’s dynamic behavior. The study demonstrates the effectiveness
of long-term automated SHM for complex bridges and highlights the need to consider environmental and operational variability, contributing to the development of reliable, scalable strategies for infrastructure health
assessment.





