  In recent years, applying deep learning (DL) to assess structural damages has
gained growing popularity in vision-based structural health monitoring (SHM).
However, both data deficiency and class-imbalance hinder the wide adoption of
DL in practical applications of SHM. Common mitigation strategies include
transfer learning, over-sampling, and under-sampling, yet these ad-hoc methods
only provide limited performance boost that varies from one case to another. In
this work, we introduce one variant of the Generative Adversarial Network
(GAN), named the balanced semi-supervised GAN (BSS-GAN). It adopts the
semi-supervised learning concept and applies balanced-batch sampling in
training to resolve low-data and imbalanced-class problems. A series of
computer experiments on concrete cracking and spalling classification were
conducted under the low-data imbalanced-class regime with limited computing
power. The results show that the BSS-GAN is able to achieve better damage
detection in terms of recall and $F_\beta$ score than other conventional
methods, indicating its state-of-the-art performance.
