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.
Designing structural health monitoring (SHM) is logically equivalent to designing a civil structure. The capacity must be greater than the demand to achieve the required performance. Monitoring capacity and demand are the counterparts of structural capacity and demand in the semi-probabilistic structural design. They are defined as the uncertainty of key-parameters that represent the structure behaviour and will be estimated through the monitoring system: the capacity is the uncertainty resulting from the estimation, the demand is the design target. As far as concrete and prestressed concrete bridges are concerned, important key-parameters are long-term temperature-compensated responses, such as strain trend, displacement trend, and rotation trend. Their estimation as well as the estimation of their uncertainty can be easily performed a posteriori through Bayesian inference, once monitoring data are available. However, in the design phase measurements are not yet available. We propose an approach for designing a structural health monitoring system accounting for temperature compensation, which allow to quantify the uncertainty of structural response trends a pre-posteriori, before monitoring data are available. We analyse the impact of sensors’ accuracy, monitoring duration, and seasonal temperature variation on the expected uncertainty. Finally, we test our framework on a real-life case study, the Colle Isarco viaduct, one of the longest prestressed concrete highway bridges in the European Alpine region.
The growing interest in structural health monitoring (SHM) and the recent technological progress have encouraged the research community to study and develop innovative sensors and monitoring methods, like the acoustic emissions (AE) technique. The number of publications on this method has increased exponentially in the last decade. However, most of the experimental validations of AE techniques are based on tests carried out in laboratory conditions on specimens or individual structural elements, and the applications to full-scale bridges in operation are typically concerned with damage states that do not jeopardize their safety. In this paper, we analyze the results of AE monitoring of a full-size prestressed concrete highway bridge subjected to a load test up to its failure. The bridge was built in 1968 and regularly maintained over the years. It is representative, by type, age, and deterioration state, of similar bridges in operation on the Italian highway network. Based on these results, we discuss the effectiveness of AE monitoring of in-service structures under regular traffic and exceptional load transits. We aim to answer the following questions: (i) Can AE discriminate whether a viaduct has local damages, such as concrete cracks? (ii) Can AE identify damage initiation, for instance, during an exceptional load transit? (iii) Can AE provide qualitative and quantitative information on damage propagation? (iv) Is it worth to invest on AE monitoring rather than “traditional” monitoring, such as crack-opening sensors?
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