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 topographic measurements taken in free configuration. Focused on the case study of the Belprato 2 Viaduct, which is affected by a 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 topographical settings. Future directions for research include the exploration of real-time monitoring datasets and the integration of additional data types.
This paper explores the potential of satellite Interferometric Synthetic Aperture Radar (InSAR) technology for Structural Health Monitoring (SHM) of road bridges. While many road bridges worldwide are over half a century old and exhibit widespread deterioration, traditional contact-type sensors for SHM are installed only on a few structures, mainly due to their high cost. In recent years, remote sensing techniques, such as satellite InSAR technology, have been explored to overcome these limitations. This paper focuses on the displacements of the Po River Bridge, which is part of the Italian A22 Highway. We extract the bridge’s displacement with Multi-Temporal InSAR data processing using SAR images acquired by the Italian Cosmo-SkyMed mission. We study 8 years of displacement time series of reflective targets, Persistent Scatterers, naturally visible on the bridge without installing any instrumentation on site. We perform an exploratory analysis of the displacements of the entire area through the K-means clustering algorithms and investigate the correlation between the bridge displacements and environmental phenomena (variation of air temperature and river water flow). The results confirm the potential of satellite InSAR technology for the remote monitoring of road bridges and their surrounding area. However, they also highlight the need for a metrological validation of such technology through a direct comparison with measurements from traditional and already validated SHM systems.
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