  The recent advances in the data science field in the last few decades have
benefitted many other fields including Structural Health Monitoring (SHM).
Particularly, Artificial Intelligence (AI) such as Machine Learning (ML) and
Deep Learning (DL) methods for vibration-based damage diagnostics of civil
structures has been utilized extensively due to the observed high performances
in learning from data. Along with diagnostics, damage prognostics is also
vitally important for estimating the remaining useful life of civil structures.
Currently, AI-based data-driven methods used for damage diagnostics and
prognostics centered on historical data of the structures and require a
substantial amount of data for prediction models. Although some of these
methods are generative-based models, they are used to perform ML or DL tasks
such as classification, regression, clustering, etc. after learning the
distribution of the data. In this study, a variant of Generative Adversarial
Networks (GAN), Cycle-Consistent Wasserstein Deep Convolutional GAN with
Gradient Penalty (CycleWDCGAN-GP) model is developed to investigate the
"transition of structural dynamic signature from an undamaged-to-damaged state"
and "if this transition can be employed for predictive damage detection". The
outcomes of this study demonstrate that the proposed model can accurately
generate damaged responses from undamaged responses or vice versa. In other
words, it will be possible to understand the damaged condition while the
structure is still in a healthy (undamaged) condition or vice versa with the
proposed methodology. This will enable a more proactive approach in overseeing
the life-cycle performance as well as in predicting the remaining useful life
of structures.
