Advanced composite structures, such as foam core carbon fiber reinforced polymer composites, are increasingly being
used in applications which require high strength, high in-plane and flexural stiffness, and low weight. However, the
presence of in situ damage due to manufacturing defects and/or service conditions can complicate the failure
mechanisms and compromise their strength and reliability. In this paper, the capability of detecting damages such as
delaminations and foam-core separations in X-COR composite structures using non-destructive evaluation (NDE) and
structural health monitoring (SHM) techniques is investigated. Two NDE techniques, flash thermography and low
frequency ultrasonics, were used to detect and quantify the damage size and locations. Macro fiber composites (MFCs)
were used as actuators and sensors to study the interaction of Lamb waves with delaminations and foam-core
separations. The results indicate that both flash thermography and low frequency ultrasonics were capable of detecting
damage in X-COR sandwich structures, although low frequency ultrasonic methods were capable of detecting through
thickness damages more accurately than flash thermography. It was also observed that the presence of foam-core
separations significantly changes the wave behavior when compared to delamination, which complicates the use of wave
based SHM techniques. Further, a wave propagation model was developed to model the wave interaction with damages
at different locations on the X-COR sandwich plate.
A reliable prognostics framework is essential to prevent catastrophic failure of bridges due to scour. In the U.S., scour accounts for almost 60% of bridge failures. Currently available techniques in the literature for predicting scour are mostly based on empirical equations and deterministic regression models, like Neural Networks and Support Vector Machines, and do not predict the evolution of scour over time. In this paper, we will discuss a Gaussian process model, which includes Bayesian uncertainty for prediction of time-dependent scour evolution. We will validate the model on the experimental data conducted in four different flumes in different conditions. The robustness of the algorithm will also be demonstrated under different scenarios, like lack of training data and equilibrium scour conditions. The results indicate that the algorithm is able to predict the scour evolution with an error of less than 20% for most of the time, and 5% or less given enough training data.