Quantifying the benefit of structural health monitoring: can the value of information be negative?

Abstract

The benefit of Structural Health Monitoring (SHM) can be properly quantified using the concept of Value of Information (VoI), which is, applied to an SHM case, the difference between the utilities of operating the structure with and without the monitoring system. The aim of this contribution is to demonstrate that, in a decision-making process where two different individuals are involved in the decision chain, i.e. the owner and the manager of the structure, the VoI can be negative. Indeed, even if the two decision makers are both rational and exposed to the same background information, their optimal actions can diverge after the installation of the monitoring system due to their different appetite for risk: this scenario could generate a negative VoI, which corresponds exactly to the amount of money the owner is willing to pay to prevent the manager using the monitoring system. In this paper, starting from a literature review about how to quantify the VoI, a mathematical formulation is proposed which allows one to assess when and under which specific conditions, e.g. appropriate combination of prior information and utility functions, the VoI becomes negative. Moreover, to illustrate how this framework works, a hypothetical VoI is evaluated for the Streicker Bridge, a pedestrian bridge on the Princeton University campus equipped with a fiber optic sensing system: the results show how the predominant factor that determines a negative VoI is the different risk appetite of the two decision makers, owner and manager.

Keywords:

• Bayesian inference
• bridge management
• decision-making
• expected utility theory
• structural health monitoring
• value of information

Acknowledgements

The case study reported in this paper is based on the Streicker Bridge monitoring project, as illustrated in references (Bolognani et al., 2018; Zonta et al. 2014). The authors wish to particularly thank Prof. Branko Glisic and Prof. Sigrid Adriaenssens, Princeton University, for sharing the information on this monitoring project. Fictional owner Ophelia and manager Malcolm, who appear in Section 4, are impersonated by Denise Bolognani and Daniel Tonelli, University of Trento; their contribution is greatly acknowledged.

Disclosure statement

No potential conflict of interest was reported by the authors.