Dynamics-based cross-domain structural damage detection through deep transfer learning

Structural damage detection (SDD) still suffers from environmental uncertainties or modeling errors, causing a gap between the numerical model and the real structure. It results in performance degradation in the application of many model-based methods, which are usually designed on a numerical model and needed to be applied to a real structure. Such a situation is defined as a cross-domain SDD problem in this work. This paper aims to address the cross-domain SDD problem by designing a feature-extractor to generate both damage-sensitive and domain-invariant features, instead of trying to reduce the gap, as the traditional methods do. A domain adaptation (DA) neural network is designed and trained on the data from both the numerical model and the real structure at the same time. In addition, no damage label of the real structure is needed. Both numerical and laboratory experiments show that the proposed method has excellent performance and outperforms the baseline model, a traditional convolutional neural network (CNN). This paper provides a new methodology to solve the cross-domain SDD problem, that is, to learn better features instead of just trying to reduce the gap.