abstract: 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. / Dissertation/Thesis / Doctoral Dissertation Mechanical Engineering 2016

The understanding of how structural components endure loads, in particular variable loads, is that these components gradually, over some period of time depending on the nature of the loading and the material, develop a microcrack. After some additional time and loading, the microcrack grows to a size that might be detected. Beyond that point, the microcrack propagates in a manner that can be reliably predicted by computer analysis codes. Consequently, one can define different stages for the life of a structural component. These are: 1) the period prior to the formation of a microcrack, 2) the period of microcrack growth, and finally 3) the period of crack growth. To date, structural health monitoring approaches that seek to detect cracks offer no insight into the extent of deterioration occurring in the initial stage that is a precursor to the formation of the microcrack or its growth. However, an approach that would facilitate monitoring the extent of the deterioration that takes place during this stage promises to improve life prediction capabilities of structural components. The challenge, thus, is to develop quantitative assessment of damage accumulation from the earliest stages of the fatigue process and to provide a structure's signature that is dependent of the damage stage. One such signature is the structure's response to forced excitation. The realization of such a goal would help in advancing structural health monitoring procedures using interrogative system identification techniques and determine sensitivities of physical parameters to damage. Additionally, vibration-based spectral quantities are related to physical properties of the structure under test. In this thesis, nonlinear response to parametric excitation is exploited for nonlinear system identification of metallic and composite beam-mass systems before damage initiation through intermediate states of damage progression to failure. Parametric identification procedure combines linear and higher order spectral analysis of vibration measurements and perturbation techniques for the derivation of the approximate solution of the system nonlinear governing differential equation. The possibility of using optical Fiber Bragg Grating sensors technology for damage localization is also assessed. Spectral moments and quantities obtained from fiber optic strain measurements are evaluated near and away from cracks to assess the relation between these moments and cracks. Variations in parameters representing natural frequency, damping and effective nonlinearities for different levels of progressive damage in a beam-mass system have been determined. Their percentage variations have been quantified to establish their sensitivities to damage initiation. The results show that damping and effective nonlinearity parameters are more sensitive to damage conditions than the natural frequency of the first mode. Crack localization is assessed by means of optical fiber technology for a composite beam-mass system. The results show that noise levels in fiber optic signals are high in comparison to strain gage signals. Of particular interest, however, is the observation that the nonlinear response is more pronounced near the cracks than away from them. / Ph. D.