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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]

Recent research in Indirect Structural Health Monitoring (ISHM) uses the dynamic response of instrumented vehicles to carry out "drive-by" monitoring of bridges. These vehicles are generally cars or trucks instrumented with different types of sensors. However, some urban bridges are inaccessible to regular vehicles. Also, cars and trucks have non-negligible weight and suspension systems that may affect the collected vibration data. Stiff, light, and standardized shared micromobility vehicles, such as bicycles and electric kick scooters, have never been explored for ISHM purposes. This paper proposes an innovative and automatic ISHM strategy based on the data collected by smartphones temporarily installed on shared micromobility vehicles. An identification procedure suitable for cloud computing is proposed to extract the dynamic parameters of bridges without needing any sensor deployment, becoming particularly appealing for monitoring a densely built environment at a territorial scale. The methodology is applied to a real footbridge in Bologna (Italy). • Crowdsourcing from human-powered vehicles is first employed for bridge monitoring. • Cloud-based platforms can gather and process crowdsourced data automatically. • The Kalman filter is used to fuse smartphone data and identify modal parameters. • A smartphone deployed on a city bike is employed to collect IMU and GPS data. • The first natural frequency and absolute mode shape of a footbridge are identified. [ABSTRACT FROM AUTHOR]