  There has been a major advance in the field of Data Science in the last few
decades, and these have been utilized for different engineering disciplines and
applications. Artificial Intelligence (AI), Machine Learning (ML) and Deep
Learning (DL) algorithms have been utilized for civil Structural Health
Monitoring (SHM) especially for damage detection applications using sensor
data. Although ML and DL methods show superior learning skills for complex data
structures, they require plenty of data for training. However, in SHM, data
collection from civil structures can be expensive and time taking; particularly
getting useful data (damage associated data) can be challenging. The objective
of this study is to address the data scarcity problem for damage detection
applications. This paper employs 1-D Wasserstein Deep Convolutional Generative
Adversarial Networks using Gradient Penalty (1-D WDCGAN-GP) for synthetic
labelled acceleration data generation. Then, the generated data is augmented
with varying ratios for the training dataset of a 1-D Deep Convolutional Neural
Network (1-D DCNN) for damage detection application. The damage detection
results show that the 1-D WDCGAN-GP can be successfully utilized to tackle data
scarcity in vibration-based damage detection applications of civil structures.
  Keywords: Structural Health Monitoring (SHM), Structural Damage Detection,
1-D Deep Convolutional Neural Networks (1-D DCNN), 1-D Generative Adversarial
Networks (1-D GAN), Wasserstein Generative Adversarial Networks with Gradient
Penalty (WGAN-GP)
