Dynamic Structural Health Monitoring, as a non-destructive method, plays a crucial role in the conservation of cultural heritage structures. Specifically, long-term monitoring enables continuous tracking of a structure's dynamic behavior, contributing to a deeper understanding of its responses to environmental factors and the progression of its condition over time.

This paper focuses on the modal identification of the Leaning Tower of Pisa. In the past, the Tower has undergone several dynamic identification campaigns; however, each was conducted sporadically and for only short durations. In July 2023, for the first time, a continuous dynamic acquisition system was installed on the Tower, featuring 21 channels dedicated to long-term monitoring of its acceleration response.

The collected data were processed using the Covariance-driven Stochastic Subspace Identification (SSI-cov) algorithm, implemented via MACEC software. This approach enabled the extraction of the Tower's natural frequencies, vibration modes, and damping ratios, as well as the tracking of their fluctuations over time. The results were then compared with findings from previous dynamic campaigns and modal data from a Finite Element Model of the Tower.

This research advances knowledge of the Tower's response to environmental influences and provides insights into soil-structure interaction, establishing a solid foundation for enhancing future structural health assessments of this and similar heritage assets.