Typically, an image formed using the backprojection algorithm is the coherent sum of every pulse’s contribution to every image pixel, accounting for the respective time delays and phase corrections. This allows for highly accurate image reconstruction. The modification proposed, differs in that the contributions of every pulse are concatenated to form a 3D radar data cube, instead of being coherently summed. This approach allows for the precise analysis of how the phase of individual target pixels change over time. In this work, the phase is utilized to accurately reconstruct the amplitude and frequency of a vibrating target. This method is demonstrated on both simulated data and compensated phase history data (CPHD) acquired by Capella Space.
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.
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?
The A22 Colle Isarco Viaduct is one of the most important infrastructural links in Italy, of strategic importance on the European route E45, connecting Northern Europe to Italy. A disruption of this bridge caused by a damage event would result in a critical increase in traffic congestion, with negative consequences for users and environment. To optimize its management after a possible damaging event, we developed an innovative decision support system (DSS), based on the data from a multi-technology structural monitoring system, which includes a robotized topographic system, a fibre optic sensor network and a thermometer network. The DSS analyses the monitoring data, assesses the probabilities that the bridge is damaged or not by using formal Bayesian inference, and identifies the optimal action according to the axioms of expected utility theory (EUT). This DSS is one of the first of its kind developed in Europe and can help in optimizing the traffic management along the A22 highway while enhancing users’ safety and reducing the bridge maintenance costs. It highlights in real time abnormal states of the bridge and allows the owner to act promptly with inspection, maintenance or repair, only when strictly necessary. We developed this DSS in collaboration with Autostrada del Brennero SpA, and although designed for a specific case study, its scope is very broad and can be applied to any problem of infrastructure management which requires optimal decision based on uncertain information under safety and economic constraints.
Only very recently our community has acknowledged that the benefit of Structural Health Monitoring (SHM) can be properly quantified using the concept of Value of Information (VoI). The VoI is the difference between the utilities of operating the structure with and without the monitoring system, usually referred to as preposterior utility and prior utility. In calculating the VoI, a commonly understood assumption is that all the decisions to concerning system installation and operation are taken by the same rational agent. In the real world, the individual who decides on buying a monitoring system (the owner) is often not the same individual (the manager) who will actually use it. Even if both agents are rational and exposed to the same background information, they may behave differently because of their different risk aversion. We propose a formulation to evaluate the VoI from the owner’s perspective, in the case where the manager differs from the owner with respect to their risk prioritisation. Moreover, we apply the results on a real-life case study concerning the Streicker Bridge, a pedestrian bridge on Princeton University campus, in USA. This framework aims to help the owner in quantifying the money saved by entrusting the evaluation of the state of the structure to the monitoring system, even if the manager’s behaviour toward risk is different from the owner’s own, and so are his or her management decisions. The results of the case study confirm the difference in the two ways to quantify the VoI of a monitoring system.
Decision making investigates choices that have uncertain consequences and that cannot be completely predicted. Rational behavior may be described by the so-called expected utility theory (EUT), whose aim is to help choosing among several solutions to maximize the expectation of the consequences. However, Kahneman and Tversky developed an alternative model, called prospect theory (PT), showing that the basic axioms of EUT are violated in several instances. In respect of EUT, PT takes into account irrational behaviors and heuristic biases. It suggests an alternative approach, in which probabilities are replaced by decision weights, which are strictly related to the decision maker’s preferences and may change for different individuals. In particular, people underestimate the utility of uncertain scenarios compared to outcomes obtained with certainty, and show inconsistent preferences when the same choice is presented in different forms. The goal of this paper is precisely to analyze a real case study involving a decision problem regarding the Streicker Bridge, a pedestrian bridge on Princeton University campus. By modelling the manager of the bridge with the EUT first, and with PT later, we want to verify the differences between the two approaches and to investigate how the two models are sensitive to unpacking probabilities, which represent a common cognitive bias in irrational behaviors.
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