Balanced semisupervised generative adversarial network for damage assessment from low-data imbalanced-class regime

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, oversampling, and undersampling, 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 semisupervised GAN (BSS-GAN). It adopts the semisupervised 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.

Automated summary

In recent years, applying deep learning to assess structural damages in vision-based structural health monitoring has become popular. However, data deficiency and class imbalance have hindered the wide adoption of deep learning in this field. The balanced semisupervised GAN (BSS-GAN) introduced in this work shows better damage detection performance compared to conventional methods.