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Gathering properly labelled, adequately rich, and case-specific data forsuccessfully training a data-driven or hybrid model for structural healthmonitoring (SHM) applications is a challenging task. We posit that a TransferLearning (TL) method that utilizes available data in any relevant source domainand directly applies to the target domain through domain adaptation can providesubstantial remedies to address this issue. Accordingly, we present a novel TLmethod that differentiates between the source's no-damage and damage cases andutilizes a domain adaptation (DA) technique. The DA module transfers theaccumulated knowledge in contrasting no-damage and damage cases in the sourcedomain to the target domain, given only the target's no-damage case.High-dimensional features allow employing signal processing domain knowledge todevise a generalizable DA approach. The Generative Adversarial Network (GAN)architecture is adopted for learning since its optimization processaccommodates high-dimensional inputs in a zero-shot setting. At the same time,its training objective conforms seamlessly with the case of no-damage anddamage data in SHM since its discriminator network differentiates between real(no damage) and fake (possibly unseen damage) data. An extensive set ofexperimental results demonstrates the method's success in transferringknowledge on differences between no-damage and damage cases across threestrongly heterogeneous independent target structures. The area under theReceiver Operating Characteristics curves (Area Under the Curve - AUC) is usedto evaluate the differentiation between no-damage and damage cases in thetarget domain, reaching values as high as 0.95. With no-damage and damage casesdiscerned from each other, zero-shot structural damage detection is carriedout. The mean F1 scores for all damages in the three independent datasets are0.978, 0.992, and 0.975.

Zero-Shot Transfer Learning for Structural Health Monitoring using Generative Adversarial Networks and Spectral Mapping