Automated OMA Through SSI-COV Algorithm of a Warren Truss Railway Bridge Exploiting Free Decay Response

The aging of bridges, coupled with increased traffic and the imperative for sustainable practices, has sparked a growing interest in Structural Health Monitoring (SHM). Modal parameters, recognized as key indicators of the structural performance of bridges and viaducts, possess the capability to unveil changes in their physical and mechanical properties resulting from damage occurrence. In this context, Automated Operational Modal Analysis (AOMA) emerges as a powerful tool for continuously identifying structural modal parameters and tracking their evolution over time. This paper proposes a robust method, based on Covariance-driven Stochastic Subspace Identification (SSI-COV) algorithm, designed for the continuous extraction of modal parameters from a Warren truss railway bridge. A permanent SHM system has been installed on the bridge, enabling the monitoring of environmental effects on the estimated modal parameters. The algorithm takes bridge free-decay responses following train passage as input, yielding promising results. These results are compared with the findings presented in a previous study that employed a peak-picking strategy.