  The practical application of structural health monitoring (SHM) is often
limited by the availability of labelled data. Transfer learning - specifically
in the form of domain adaptation (DA) - gives rise to the possibility of
leveraging information from a population of physical or numerical structures,
by inferring a mapping that aligns the feature spaces. Typical DA methods rely
on nonparametric distance metrics, which require sufficient data to perform
density estimation. In addition, these methods can be prone to performance
degradation under class imbalance. To address these issues, statistic alignment
(SA) is discussed, with a demonstration of how these methods can be made robust
to class imbalance, including a special case of class imbalance called a
partial DA scenario. SA is demonstrated to facilitate damage localisation with
no target labels in a numerical case study, outperforming other
state-of-the-art DA methods. It is then shown to be capable of aligning the
feature spaces of a real heterogeneous population, the Z24 and KW51 bridges,
with only 220 samples used from the KW51 bridge. Finally, in scenarios where
more complex mappings are required for knowledge transfer, SA is shown to be a
vital pre-processing tool, increasing the performance of established DA
methods.
