  Quantifying the value of the information extracted from a structural health
monitoring (SHM) system is an important step towards convincing decision makers
to implement these systems. We quantify this value by adaptation of the
Bayesian decision analysis framework. In contrast to previous works, we model
in detail the entire process of data generation to processing, model updating
and reliability calculation, and investigate it on a deteriorating bridge
system. The framework assumes that dynamic response data are obtained in a
sequential fashion from deployed accelerometers, subsequently processed by an
output-only operational modal analysis scheme for identifying the system's
modal characteristics. We employ a classical Bayesian model updating
methodology to sequentially learn the deterioration and estimate the structural
damage evolution over time. This leads to sequential updating of the structural
reliability, which constitutes the basis for a preposterior Bayesian decision
analysis. Alternative actions are defined and a heuristic-based approach is
employed for the life-cycle optimization. By solving the preposterior Bayesian
decision analysis, one is able to quantify the benefit of the availability of
long-term SHM vibrational data. Numerical investigations show that this
framework can provide quantitative measures on the optimality of an SHM system
in a specific decision context.
