Bayesian Data Fusion Approach for InSAR and Topographic Bridge Displacement Monitoring

This paper introduces a Bayesian data fusion methodology for the monitoring of bridge displacements, employing a synergistic combination of satellite Interferometric Synthetic Aperture Radar (InSAR) and total station measurements taken in free configuration. Focused on the case study of the Belprato 2 Viaduct, which is affected by an extremely slow-moving landslide, this research demonstrates the potential of integrating diverse data sources to overcome the limitations posed by these monitoring techniques considered as alone. Our approach leverages the frequency and the remote, non-intrusive nature of InSAR technology and the accuracy of topographic surveys to obtain a high-resolution, three-dimensional bridge displacements caused by the landslide and temperature variations. The Bayesian framework facilitates the optimal fusion of these datasets, accounting for their respective uncertainties and different temporal resolutions. Moreover, it allows to include the information a priori on the landslide movements resulting for previous geological and geotechnical studies. The results from this study reveal significant improvements in the accuracy and reliability of displacement measurements, highlighting the benefits of data fusion for structural health monitoring. This paper highlights the importance of innovative monitoring solutions in the context of aging infrastructure, increasing environmental and traffic challenges, and complex morphological settings. Future directions for research include the exploration of real-time monitoring datasets and the integration of additional data types.