  Fatigue crack growth is one of the most common types of deterioration in
metal structures with significant implications on their reliability. Recent
advances in Structural Health Monitoring (SHM) have motivated the use of
structural response data to predict future crack growth under uncertainty, in
order to enable a transition towards predictive maintenance. Accurately
representing different sources of uncertainty in stochastic crack growth (SCG)
processes is a non-trivial task. The present work builds on previous research
on physics-based SCG modeling under both material and load-related uncertainty.
The aim here is to construct computationally efficient, probabilistic surrogate
models for SCG processes that successfully encode these different sources of
uncertainty. An approach inspired by latent variable modeling is employed that
utilizes Gaussian Process (GP) regression models to enable the surrogates to be
used to generate prior distributions for different Bayesian SHM tasks as the
application of interest. Implementation is carried out in a numerical setting
and model performance is assessed for two fundamental crack SHM problems;
namely crack length monitoring (damage quantification) and crack growth
monitoring (damage prognosis).
