Structural Health Monitoring (SHM) today represents a fundamental tool for the continuous evaluation of the performance level of civil structures and infrastructures throughout their life cycle. In recent years, several advanced techniques have been developed to address the different phases of monitoring, including the optimization of the testing setup, the denoising and pre-processing of signals, the identification of mechanical properties, damage detection based on data-driven approaches, and integration with digital twinning procedures. However, such methodologies are often applied in a fragmented way, through heterogeneous tools, making their operational integration and long-term evolution complex. In real and long-term monitoring contexts, the need to modify, update or replace algorithms and analysis techniques is highly probable, due to changes in operating conditions, in the instrumentation configuration or in monitoring objectives, but also due to the evolution of research in this field.