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Structural Engineering Commons

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Structural health monitoring

University of Central Florida

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Full-Text Articles in Structural Engineering

Damage Detection Methodologies For Structural Health Monitoring Of Thin-Walled Pressure Vessels, Arturo Modesto Jan 2015

Damage Detection Methodologies For Structural Health Monitoring Of Thin-Walled Pressure Vessels, Arturo Modesto

Electronic Theses and Dissertations

There is a need in exploring structural health monitoring technologies for the composite structures particularly aged Composite Overwrapped Pressure Vessels (COPVs) for the current and future implementation of COPVs for space missions. In this study, the research was conducted in collaboration with NASA Kennedy Space Center and also NASA Marshall Space and Flight Center engineers. COPVs have been used to store inert gases like helium (for propulsion) and nitrogen (for life support) under varying degrees of pressure onboard the orbiter since the beginning of the Space Shuttle Program. After the Columbia accident, the COPVs were re-examined and different studies (e.g. …


Analytical And Experimental Study Of Monitoring For Chain-Like Nonlinear Dynamic Systems, Bryan Paul Jan 2013

Analytical And Experimental Study Of Monitoring For Chain-Like Nonlinear Dynamic Systems, Bryan Paul

Electronic Theses and Dissertations

Inverse analysis of nonlinear dynamic systems is an important area of research in the eld of structural health monitoring for civil engineering structures. Structural damage usually involves localized nonlinear behaviors of dynamic systems that evolve into different classes of nonlinearity as well as change system parameter values. Numerous parametric modal analysis techniques (e.g., eigensystem realization algorithm and subspace identification method) have been developed for system identification of multi-degree-of-freedom dynamic systems. However, those methods are usually limited to linear systems and known for poor sensitivity to localized damage. On the other hand, non-parametric identification methods (e.g., artificial neural networks) are advantageous …