Composite structures, particularly carbon-fiber reinforced polymers (CFRPs) have been subject to significant development in recent years. They have become increasingly reliable, durable, and versatile, finding a role in a wide variety of applications. When compared to conventional materials, CFRPs have several advantages, including extremely high strength, high in-plane and flexural stiffness, and very low weight. However, the application of CFRPs and other fiber-matrix composites is complicated due to the manner in which damage propagates throughout the structure, and the associated difficulty in identifying and repairing such damages prior to structural failure. In this paper, a methods of detecting and localizing delaminations withint a complex foam-core composite structure using non-destructive evaluation (NDE) and structural health montoring (SHM) is investigated. The two NDE techniques utilized are flash thermography and low frequency ultrasonic C-Scan, which were used to confirm the location of seeded damages within the specimens and to quantify the size of the damages. Macro fiber composite sensors (MFCs) and piezoelectric sensors (PZTs) were used as actuators and sensors in pitch-catch and pulse-echo configurations in order to study mode conversions and wave reflections of the propagated Lamb waves when interacting with interply delaminations and foam-core separations. The final results indicated that the investigated NDE and SHM techniques are capable of detecting and quantifying damages within complex X-COR composites, with the SHM techniques having the potential to be used \textit{in situ} with a high degree of accuracy. It was also observed that the presence of the X-COR significantly alters the behavior of the wave when compared to a standard CFRP composite plate, making it necessary to account for any variations if wave-base techniques are to be used for damage detection and quantification. Lastly, a time-space model was created to model the wave interactions with damages located within X-COR complex sandwich composites.

The goal of this research is to couple a physics-based model with adaptive algorithms to develop a more accurate and robust technique for structural health monitoring (SHM) in composite structures. The purpose of SHM is to localize and detect damage in structures, which has broad applications to improvements in aerospace technology. This technique employs PZT transducers to actuate and collect guided Lamb wave signals. Matching pursuit decomposition (MPD) is used to decompose the signal into a cross-term free time-frequency relation. This decoupling of time and frequency facilitates the calculation of a signal's time-of-flight along a path between an actuator and sensor. Using the time-of-flights, comparisons can be made between similar composite structures to find damaged regions by examining differences in the time of flight for each path between PZTs, with respect to direction. Relatively large differences in time-of-flight indicate the presence of new or more significant damage, which can be verified using a physics-based approach. Wave propagation modeling is used to implement a physics based approach to this method, which is coupled with adaptive algorithms that take into account currently existing damage to a composite structure. Previous SHM techniques for composite structures rely on the assumption that the composite is initially free of all damage on both a macro and micro-scale, which is never the case due to the inherent introduction of material defects in its fabrication. This method provides a novel technique for investigating the presence and nature of damage in composite structures. Further investigation into the technique can be done by testing structures with different sizes of damage and investigating the effects of different operating temperatures on this SHM system.

There is a concerted effort in developing robust systems health monitoring/management (SHM) technology as a means to reduce the life cycle costs, improve availability, extend life and minimize downtime of various platforms including aerospace and civil infrastructure. The implementation of a robust SHM system requires a collaborative effort in a variety of areas such as sensor development, damage detection and localization, physics based models, and prognosis models for residual useful life (RUL) estimation. Damage localization and prediction is further complicated by geometric, material, loading, and environmental variabilities. Therefore, it is essential to develop robust SHM methodologies by taking into account such uncertainties. In this research, damage localization and RUL estimation of two different physical systems are addressed: (i) fatigue crack propagation in metallic materials under complex multiaxial loading and (ii) temporal scour prediction near bridge piers. With little modifications, the methodologies developed can be applied to other systems.

Current practice in fatigue life prediction is based on either physics based modeling or data-driven methods, and is limited to predicting RUL for simple geometries under uniaxial loading conditions. In this research, crack initiation and propagation behavior under uniaxial and complex biaxial fatigue loading is addressed. The crack propagation behavior is studied by performing extensive material characterization and fatigue testing under in-plane biaxial loading, both in-phase and out-of-phase, with different biaxiality ratios. A hybrid prognosis model, which combines machine learning with physics based modeling, is developed to account for the uncertainties in crack propagation and fatigue life prediction due to variabilities in material microstructural characteristics, crack localization information and environmental changes. The methodology iteratively combines localization information with hybrid prognosis models using sequential Bayesian techniques. The results show significant improvements in the localization and prediction accuracy under varying temperature.

For civil infrastructure, especially bridges, pier scour is a major failure mechanism. Currently available techniques are developed from a design perspective and provide highly conservative scour estimates. In this research, a fully probabilistic scour prediction methodology is developed using machine learning to accurately predict scour in real-time under varying flow conditions.

Because metallic aircraft components are subject to a variety of in-service loading conditions, predicting their fatigue life has become a critical challenge. To address the failure mode mitigation of aircraft components and at the same time reduce the life-cycle costs of aerospace systems, a reliable prognostics framework is essential. In this paper, a hybrid prognosis model that accurately predicts the crack growth regime and the residual-useful-life estimate of aluminum components is developed. The methodology integrates physics-based modeling with a data-driven approach. Different types of loading conditions such as constant amplitude, random, and overload are investigated. The developed methodology is validated on an Al 2024-T351 lug joint under fatigue loading conditions. The results indicate that fusing the measured data and physics-based models improves the accuracy of prediction compared to a purely data-driven or physics-based approach.