Integrating Remote Sensing and on-site data to derive historical time series for Long-Term structural monitoring

In the long-term monitoring of full-scale structures, it is essential to analyse the temporal evolution of specific parameters to accurately evaluate the health condition of the structure. A comprehensive understanding requires sufficient amounts of heterogeneous data describing different aspects of structural behaviour. In this context, the integration of remotely acquired data, such as satellite data, into monitoring protocols could be strategic for filling gaps in on-site measurements, offering extended and continuous time series. Considering the structure, the environment, and the soil as an integrated system, this study proposes a method for reconstructing historical time series of soil parameters that could influence structural response. The model used is based on sparse Bayesian learning (SBL) and combines remotely sensed data with local measurements, achieving an accurate reconstruction of in situ parameters. This approach allows for the simulation of soil and structural behaviour even in the absence of direct data, improving the ability to distinguish between physiological variations and real signs of damage. This is essential to avoid interpretative errors that could compromise the effectiveness of monitoring and the safety of structures.