Key findings from long-term operational modal analysis of a landmark steel arch bridge in Italy

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 Many aging bridges, designed under outdated standards, demand advanced monitoring to ensure continued performance.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. • Continuous OMA on a landmark long-span steel arch bridge in central Italy. • In-depth assessment of the SHM strategy and automated modal identification. • Long-term frequency trends reveal nonlinear correlations with temperature and traffic. • Statistical models compared to track modal shifts and detect anomalies via T 2 control charts.