Dynamic identification of prestressed reinforced concrete railway bridges through Automated Operational Modal Analysis: an example on two case studies

Structural Health Monitoring (SHM) of strategic transportation infrastructures is becoming increasingly important due to ageing and degradation, particularly in the case of railway bridges and viaducts that support high-speed train operations. This study presents the experimental dynamic identification of two prestressed reinforced concrete (PRC) railway bridges, representative of two common short-to-medium span typologies. The accelerometric data were acquired under operational conditions, hence, recording both ambient vibrations and train passage-induced high-amplitude vibrations. After discarding these latter disturbances, Ambient Vibration Tests (AVT) were performed through a recently-introduced Automated Operational Modal Analysis (AOMA) algorithm to identify the modal parameters (natural frequencies, damping ratios, and mode shapes), which serve as damage-sensitive features. The results of this dynamic identification are then benchmarked against those obtained with state-of-the-art commercial software (ARTeMIS), confirming the accuracy and reliability of the proposed approach.