Structural Health Monitoring (SHM) of existing bridges increasingly relies on dynamic measurements to assess structural performance and detect potential damage. However, the practical implementation of long-term vibration-based monitoring is still constrained by the volume of data required and the complexity of continuous acquisition systems. In the context of ensuring the safety and performance of existing bridge infrastructure, vibration-based monitoring offers a powerful tool for detecting changes in structural behavior. This study presents an extended investigation of dynamic monitoring applied to composite steel–concrete viaducts, focusing particularly on the signal-analysis framework and methodological enhancements. Short-duration accelerometric records are processed through an automated signal-selection pipeline and advanced modal-parameter extraction algorithms to yield identification of modal features. Emphasis is placed on the statistical evaluation of modal-parameter stability, effects of operational and environmental variability, and the potential for long-term trend detection. The results highlight the limits of short-length recordings when OMA techniques are applied. Nevertheless, appropriate signal processing and data handling can provide acceptable insights into the dynamic characteristics of large bridge systems. The methodological findings provide a foundation for improved monitoring workflows, showing the amount of information that can be retrieved using a cost-effective hardware deployment and supporting further development toward structural digital twins.