ABSTRACT
Health monitoring has, over the last 2-3 decades, become a topic of significant interest within the structural engineering research community, but also in the broader areas of civil and mechanical engineering. Whereas the merits of Structural Health Monitoring (SHM) are generally appreciated in qualitative terms there has, as of yet, not been reported much fundamental research on the quantification of the benefit of SHM. The present paper addresses the quantification of SHM with basis in the Bayesian pre-posterior decision analysis. The starting points are historical and recent developments in the fields of SHM and the quantification of value of information as well as the identification of typical situations in structural engineering in which SHM has the potential to provide value beyond its costs. Subsequently, the theoretical framework which allows for the quantification of the value of information collected through SHM systems is developed and elaborated. It is shown how the value of information can be quantified to support the assessment and optimization of decisions on whether and how to implement SHM. To illustrate the use of the developed theoretical framework for evaluating the benefit of SHM an example is provided. The example addresses the life-cycle benefit maximization for offshore jacket structures subject to fatigue crack growth utilizing monitoring of near field fatigue stresses as a means of optimizing risk based inspection and maintenance strategies.
