The ability to detect and predict the progressive degradation of structures—whether due to natural material aging or unexpected dynamic events such as earthquakes or explosions—is of paramount importance. However, identifying structural damage through the analysis of modal parameter variations, while minimizing the influence of external factors like environmental conditions, remains a challenging and resource-intensive task. At the same time, the capacity to rapidly assess and respond to changes in structural behavior is essential for safeguarding human lives and ensuring the timely implementation of corrective actions. Quick interventions in critical situations not only mitigate risks but also minimize disruptions to structural functionality, enabling faster recovery where possible. This study introduces a methodology that leverages data directly from continuous monitoring systems, significantly reducing computational complexity. By bypassing the extraction of modal characteristics from acquired data, the proposed approach delivers near-instant feedback on dynamic behavior changes. This capability is pivotal for advancing sustainable, cost-effective, and efficient monitoring practices, enabling timely decision-making and improved structural resilience.