  As Structural Health Monitoring (SHM) being implemented more over the years,
the use of operational modal analysis of civil structures has become more
significant for the assessment and evaluation of engineering structures.
Machine Learning (ML) and Deep Learning (DL) algorithms have been in use for
structural damage diagnostics of civil structures in the last couple of
decades. While collecting vibration data from civil structures is a challenging
and expensive task for both undamaged and damaged cases, in this paper, the
authors are introducing Generative Adversarial Networks (GAN) that is built on
the Deep Convolutional Neural Network (DCNN) and using Wasserstein Distance for
generating artificial labelled data to be used for structural damage diagnostic
purposes. The authors named the developed model 1D W-DCGAN and successfully
generated vibration data which is very similar to the input. The methodology
presented in this paper will pave the way for vibration data generation for
numerous future applications in the SHM domain.

Machine Learning;Artificial Intelligence