  Structural Health Monitoring (SHM) has been continuously benefiting from the
advancements in the field of data science. Various types of Artificial
Intelligence (AI) methods have been utilized for the assessment and evaluation
of civil structures. In AI, Machine Learning (ML) and Deep Learning (DL)
algorithms require plenty of datasets to train; particularly, the more data DL
models are trained with, the better output it yields. Yet, in SHM applications,
collecting data from civil structures through sensors is expensive and
obtaining useful data (damage associated data) is challenging. In this paper,
1-D Wasserstein loss Deep Convolutional Generative Adversarial Networks using
Gradient Penalty (1-D WDCGAN-GP) is utilized to generate damage associated
vibration datasets that are similar to the input. For the purpose of
vibration-based damage diagnostics, a 1-D Deep Convolutional Neural Network
(1-D DCNN) is built, trained, and tested on both real and generated datasets.
The classification results from the 1-D DCNN on both datasets resulted to be
very similar to each other. The presented work in this paper shows that for the
cases of insufficient data in DL or ML-based damage diagnostics, 1-D WDCGAN-GP
can successfully generate data for the model to be trained on. Keywords: 1-D
Generative Adversarial Networks (GAN), Deep Convolutional Generative
Adversarial Networks (DCGAN), Wasserstein Generative Adversarial Networks with
Gradient Penalty (WGAN-GP), 1-D Convolutional Neural Networks (CNN), Structural
Health Monitoring (SHM), Structural Damage Diagnostics, Structural Damage
Detection
