Automated Operational Modal Analysis of a steel truss railway bridge employing free decay response

• Published in: Journal of Infrastructure Intelligence and Resilience Vol. 4; no. 1; p. 100145
• Main Authors: Bono, Francesco Morgan, Argentino, Antonio, Bernardini, Lorenzo, Benedetti, Lorenzo, Cazzulani, Gabriele, Somaschini, Claudio, Belloli, Marco
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2025
• Subjects: EFDD; Operational Modal Analysis; Peak-Picking; Railway bridge; SSI-COV; Steel truss bridge; Structural Health Monitoring; Structural Health Monitoring; EFDD; Peak-Picking; Operational Modal Analysis; SSI-COV; Steel truss bridge; Railway bridge
• ISSN: 2772-9915; 2772-9915
• DOI: 10.1016/j.iintel.2025.100145

The efficiency and resilience of transportation networks depend significantly on the integrity of bridges, which are increasingly threatened by ageing, traffic, and extreme climate events. Traditional visual inspections have notable limitations, necessitating the adoption of more objective methods like Structural Health Monitoring (SHM). This study explores the application of Operational Modal Analysis (OMA) to estimate the modal parameters of railway bridges, specifically using the Covariance-based Stochastic Subspace Identification (SSI-COV) algorithm. The case study involves a steel Warren truss bridge monitored over 20 months. The research demonstrates that SSI-COV, typically requiring stationary random input, can effectively utilise the bridge’s free decay responses following train passages. This approach strongly improves signal-to-noise ratio, which is vice-versa critical for railway bridges ambient vibrations due to the very low input energy, enabling precise modal parameter estimation with shorter time windows and lower-performance sensors. Results were validated against the Peak-Picking (PP) and the Enhanced Frequency Domain Decomposition (EFDD) methods, with SSI-COV identifying three additional natural frequencies and exhibiting lower dispersion in frequency estimates throughout the monitored period. Statistical analysis further indicated that using multiple free decays enhances the accuracy and reduces variability for challenging modes, while dominant modes are reliably estimated with minimal decay data. These findings endorse the combination of SSI-COV and free decays as a robust tool for detailed and long-term bridge monitoring, offering a valuable and potentially low-cost alternative to ambient vibration-based OMA techniques. •Train-induced free decays for operational modal analysis.•Comparison between SSI-COV and Peak-Picking for modal identification.•SSI-COV finds more natural frequencies and lowers dispersion than Peak-Picking.•Finds improved accuracy with more free decay samples.•Validation of SSI-COV for reliable modal parameter estimation over 20-month period.•SSI-COV achieves stable frequency estimates with limited decay data