  Obtaining the ability to make informed decisions regarding the operation and
maintenance of structures, provides a major incentive for the implementation of
structural health monitoring (SHM) systems. Probabilistic risk assessment (PRA)
is an established methodology that allows engineers to make risk-informed
decisions regarding the design and operation of safety-critical and high-value
assets in industries such as nuclear and aerospace. The current paper aims to
formulate a risk-based decision framework for structural health monitoring that
combines elements of PRA with the existing SHM paradigm. As an apt tool for
reasoning and decision-making under uncertainty, probabilistic graphical models
serve as the foundation of the framework. The framework involves modelling
failure modes of structures as Bayesian network representations of fault trees
and then assigning costs or utilities to the failure events. The fault trees
allow for information to pass from probabilistic classifiers to influence
diagram representations of decision processes whilst also providing nodes
within the graphical model that may be queried to obtain marginal probability
distributions over local damage states within a structure. Optimal courses of
action for structures are selected by determining the strategies that maximise
expected utility. The risk-based framework is demonstrated on a realistic
truss-like structure and supported by experimental data. Finally, a discussion
of the risk-based approach is made and further challenges pertaining to
decision-making processes in the context of SHM are identified.
