Transfer Learning Across Heterogeneous Structures Through Adversarial Training

Abstract

Transfer learning (TL) methods have become increasingly crucial for the challenges in gathering accurately labeled data from various structures in structural health monitoring (SHM) tasks, such as structural damage detection (SDD). The structures must meet specific similitude criteria for the proposed TL technique's effectiveness in current one-to-one domain approaches. To overcome this challenge, the authors have developed a novel TL method that utilizes raw vibrational features and raw-feature-to-raw-feature domain adaptation (DA) through spectral mapping. This approach offers a generalizable TL strategy that works across vastly different structures. The authors used generative adversarial network (GAN) architecture for the "learning," as it can accommodate high-dimensional inputs in a zero-shot setting. The proposed TL approach was successfully evaluated over three structural health monitoring (SHM) benchmarks. Area under the curve (AUC) of the receiver operating characteristics (ROC) curve resulted in a threshold-bias-free estimation of SDD models retaining as much as 99% of the source model's AUC through its application across different systems with diverse damage-representative data cases.