This dissertation presents a novel “Intelligent Parameter Varying” (IPV) health monitoring and damage detection technique that accurately detects the existence, location, and time of damage occurrence without any assumptions about the constitutive nature of structural non-linearities. This technique combines the advantages of parametric techniques with the non-parametric capabilities of artificial neural networks by incorporating artificial neural networks into a traditional parametric model.; This IPV technique is demonstrated using a lumped-mass structural model with an embedded array of artificial neural networks. These networks identify the non-linear and time-varying storing forces that would be difficult or impossible to model using traditional modeling techniques. This approach preserves direct associations between the model and the underlying system dynamics, making it ideally suited for health monitoring. Backpropagation of error is used to identify the “optimal” network parameters from recorded acceleration responses.; Chapter 1 presents an introduction to commonly used health monitoring and damage detection strategies, discusses their advantages and shortcomings, and identifies the building blocks of an effective health monitoring and damage detection strategy. Chapter 2 presents the principles of modeling and system identification. Different modeling and optimization techniques are introduced and their relevance to health monitoring and damage detection are identified. Chapter 3 introduces artificial neural networks, in particular Radial Basis Function Networks (RBFNs), for function approximation as related to the development of the IPV technique. Chapter 4 presents the development and implementation of the IPV technique. It includes development of (1) a computational model of a typical three-story, base-excited structure, (2) computational models for elastic, elasto-plastic, and hysteretic restoring forces, (3) structural damage mechanisms, (4) structural response simulations to synthetic and recorded ground excitations, and (5) the IPV technique implementation. Chapter 5 is devoted to studying the effects of changes in artificial neural network parameters on IPV accuracy and performance. Chapter 6 is devoted to studying the effects of measurement noise on IPV accuracy. Chapter 7 identifies the main advantages of IPV over other techniques and provides future research directions. (Abstract shortened by UMI.)
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