  Gaining the ability to make informed decisions on operation and maintenance
of structures provides motivation for the implementation of structural health
monitoring (SHM) systems. However, descriptive labels for measured data
corresponding to health-states of the monitored system are often unavailable.
This issue limits the applicability of fully-supervised machine learning
paradigms for the development of statistical classifiers to be used in
decision-support in SHM systems. One approach to dealing with this problem is
risk-based active learning. In such an approach, data-label querying is guided
according to the expected value of perfect information for incipient data
points. For risk-based active learning in SHM, the value of information is
evaluated with respect to a maintenance decision process, and the data-label
querying corresponds to the inspection of a structure to determine its health
state.
  In the context of SHM, risk-based active learning has only been considered
for generative classifiers. The current paper demonstrates several advantages
of using an alternative type of classifier -- discriminative models. Using the
Z24 Bridge dataset as a case study, it is shown that discriminative classifiers
have benefits, in the context of SHM decision-support, including improved
robustness to sampling bias, and reduced expenditure on structural inspections.
