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• Structural health monitoring systems can be damaged and provide altered data. • The value of information framework is here extended to account for sensor faults. • The uncertainty in assessing the state of the monitoring systems is accounted for. • Knowing the state of the monitoring system increases the value of information. Structural Health Monitoring (SHM) systems are adopted to acquire timely and continuous data on the state of civil structures, aerospace vehicles, and industrial machines, which deteriorate due to slow processes, such as corrosion and fatigue, and shock events, including natural and man-made disasters. The components of SHM systems themselves are exposed to deterioration after their installation; thereby, they might provide altered information to decision-makers. To account for this issue, Sensor Validation Tools (SVTs) have been developed to give insight into the actual condition of the SHM systems. In the last decade, researchers have exploited the Value of Information (VoI) from Bayesian decision theory to quantify the benefit of the information provided by an SHM system, implicitly assuming that it is working correctly when interrogated. The benefit of the information provided by SVTs on the state of an SHM system has never been investigated. This paper addresses this topic and extends the classical VoI framework to quantify the additional benefit brought by the information on the state of the SHM system to the decision problems it is meant to support. A numerical analysis, as well as a methodology demonstration on a real bridge, are presented to discuss the framework. [ABSTRACT FROM AUTHOR]

Widespread monitoring of bridges is yet rarely employed at a territorial level due to the high costs of monitoring systems. However, the aging of civil infrastructures, combined with the growing traffic demand, poses the need for a simple and automatic tool that helps emergency management. In this paper, an integrated algorithm for the identification of dynamic and dense quasi-static structural features exploiting moving vehicles is proposed. Filtering raw acceleration structural responses, triggered by passing vehicles, enables the identification of modal parameters and curvature influence lines. The procedure can be implemented efficiently as its main computational core consists of convolutions. The definition of a curvature-based damage index leads to accurate localization and quantification of structural anomalies using few sensors. The proposed procedure is tested on three viaducts of the Italian A24 motorway. Moreover, a numerical model is studied to evaluate the potentialities of the strategy for damage localization. • An integrated method to identify modal parameters and influence lines is proposed. • Dense structural features of bridges are identified by filtering acceleration data. • Quasi-static features can be directly employed as dense damage indicators. • Accurate damage localization can be achieved efficiently, using few sensors. • Numerical and experimental case studies show the potential of the method proposed. [ABSTRACT FROM AUTHOR]