Assessing the Value of Information for long-term structural health monitoring

TLDR: The basic concepts involved, issues related to monitoring of civil structures, address the problem of non-linearity of the cost-to-utility mapping, and introduce an approximate Monte Carlo approach suitable for the implementation of time-consuming predictive models are presented.

Abstract: In the field of Structural Health Monitoring, tests and sensing systems are intended as tools providing diagnoses, which allow the operator of the facility to develop an efficient maintenance plan or to require extraordinary measures on a structure. The effectiveness of these systems depends directly on their capability to guide towards the most optimal decision for the prevailing circumstances, avoiding mistakes and wastes of resources. Though this is well known, most studies only address the accuracy of the information gained from sensors without discussing economic criteria. Other studies evaluate these criteria separately, with only marginal or heuristic connection with the outcomes of the monitoring system. The concept of “Value of Information” (VoI) provides a rational basis to rank measuring systems according to a utility-based metric, which fully includes the decision-making process affected by the monitoring campaign. This framework allows, for example, an explicit assessment of the economical justifiability of adopting a sensor depending on its precision. In this paper we outline the framework for assessing the VoI, as applicable to the ranking of competitive measuring systems. We present the basic concepts involved, highlight issues related to monitoring of civil structures, address the problem of non-linearity of the cost-to-utility mapping, and introduce an approximate Monte Carlo approach suitable for the implementation of time-consuming predictive models.