Structural Health Monitoring (SHM) is generally presented as a powerful tool that allows bridge managers to make decisions on maintenance, reconstruction and repair of their assets. The benefit of SHM can be properly quantified using the concept of Value of Information (VoI), i.e. the difference between the utilities of operating the structure with and without the monitoring system. In calculating the VoI, two understood assumptions are that all decisions concerning system installation and operation are taken by the same rational agent and that “information never hurts”, i.e. VoI is guaranteed to be non-negative. However, 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 use it once the system has been installed, and they may be-have differently because of their different risk aversion. We develop a formulation to properly evaluate the VoI from the owner perspective, when the manager is a different individual. We demonstrate that in a decision-making process where the two individuals involved share exactly the same information, but behave differently, the VoI can be negative and that we can always find a combination of prior probabilities and utility functions which ultimately yields a negative conditional VoI. Indeed, even if the two agents have an agreement a priori, due to their different behaviors, their optimal actions can diverge after the installation of the monitoring system. We apply this formulation on a real-life case study concerning the Streicker Bridge (NJ, USA).
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.
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.
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.