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Bridge scour is a leading cause of failure for bridges over waterways and is notoriously difficult to detect with accuracy. Dynamic Structural Health Monitoring (SHM) of bridges for scour has gained traction in recent years as monitoring systems have improved and the reliability of measurements increased. Due to the large number of bridges on typical networks and the limited financial resources within asset management agencies, decision-makers must prioritize certain structures when it comes to management in the event of flooding. The decision to install a dynamic SHM system on a bridge must be balanced by the financial benefit of doing so, as limited resources often need to be carefully rationed. In this paper, a methodology is proposed to evaluate such benefit based on the Value of Information (VoI) from Bayesian decision analysis. A case study is presented whereby a dynamic SHM system is considered to be installed on a typical bridge with the aim to support emergency management operations during flooding. The proposed methodology allows computation of the financial benefit of installing a dynamic SHM system over a certain reference period, thus accounting for multiple flood events and scenarios. The various elements of the procedure are discussed in detail. [ABSTRACT FROM AUTHOR]

In the aftermath of a seismic event, decision-makers have to decide quickly among alternative management actions with limited knowledge on the actual health condition of buildings. Each choice entails different direct and indirect consequences. For example, if a building sustains low damage in the mainshock but people are not evacuated, casualties may occur if aftershocks lead the structure to fail. On the other hand, the evacuation of a structurally sound building could lead to unnecessary financial losses due to business and occupancy interruption. A monitoring system can provide information about the condition of the building after an earthquake that can support the choice between several competing alternatives, targeting the minimization of consequences. This paper proposes a framework for quantifying the benefit of installing a permanent seismic structural health monitoring (S2HM) system to support building evacuation operations after a seismic event. Decision-makers can use this procedure to preventively evaluate the benefit of an SHM system and decide about the worthiness of its installation. [ABSTRACT FROM AUTHOR]

The management of civil infrastructures in the aftermath of a seismic event is a concern for decision makers, which have to choose quickly among alternative actions with limited knowledge on the actual structural conditions. The availability of real time Structural Health Monitoring (SHM) data on the asset might be particularly useful. However, SHM data are not collected for free, and the cost of the SHM system should be compared with its associated benefit. A powerful tool to estimate such benefit is the Value of Information (VoI) from Bayesian decision theory. This paper provides a methodology to compute the VoI of SHM for seismic emergency management of roadway bridges. This methodology can be used by decision makers before the installation of a seismic SHM system to quantify the cost benefit of doing so and thus optimize the allocation of economic resources. Results show that the VoI is high when the expected costs of the decision alternatives (such as 'keep the bridge open' or 'close the bridge') evaluated without the SHM information are comparable. In this condition, which is highly dependent on the seismic hazard and on emergency management costs at stake, the SHM information provides the maximum support to decision making. [ABSTRACT FROM AUTHOR]

• 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]