  Gathering properly labelled, adequately rich, and case-specific data for
successfully training a data-driven or hybrid model for structural health
monitoring (SHM) applications is a challenging task. We posit that a Transfer
Learning (TL) method that utilizes available data in any relevant source domain
and directly applies to the target domain through domain adaptation can provide
substantial remedies to address this issue. Accordingly, we present a novel TL
method that differentiates between the source's no-damage and damage cases and
utilizes a domain adaptation (DA) technique. The DA module transfers the
accumulated knowledge in contrasting no-damage and damage cases in the source
domain to the target domain, given only the target's no-damage case.
High-dimensional features allow employing signal processing domain knowledge to
devise a generalizable DA approach. The Generative Adversarial Network (GAN)
architecture is adopted for learning since its optimization process
accommodates high-dimensional inputs in a zero-shot setting. At the same time,
its training objective conforms seamlessly with the case of no-damage and
damage data in SHM since its discriminator network differentiates between real
(no damage) and fake (possibly unseen damage) data. An extensive set of
experimental results demonstrates the method's success in transferring
knowledge on differences between no-damage and damage cases across three
strongly heterogeneous independent target structures. The area under the
Receiver Operating Characteristics curves (Area Under the Curve - AUC) is used
to evaluate the differentiation between no-damage and damage cases in the
target domain, reaching values as high as 0.95. With no-damage and damage cases
discerned from each other, zero-shot structural damage detection is carried
out. The mean F1 scores for all damages in the three independent datasets are
0.978, 0.992, and 0.975.
