This dissertation is an investigation of the use of genetic algorithms for the purposes of finite element model refinement and pre-modal test planning. The objective of a model refinement technique is to use information, about a structure, obtained during a vibration test to update the analytical model. The product of this process is an updated model of a structure which possesses dynamic properties closer to the dynamics obtained from the modal test of the structure. A genetic algorithm is used to vary finite element structural parameters to obtain an updated model with measured modal properties.; Although one purpose of a modal test is to use the information to update finite element models, the information obtained may be used for other purposes such as damage assessment, critical loads and frequency determination, and vibration control design. The type of information to be realized from a vibration test may well govern how and where the structure is excited and observed. The principal purpose of the current work is to explore the subject of pre-modal test planning for excitation and sensor placement. An overview of several existing sensor and excitation placement techniques is presented as a platform for the current study. The sensor placement techniques include effective independence, kinetic energy, and eigenvector product and the excitation placement techniques include eigenvector product, kinetic energy, and driving-point residue. Two new sensor and two new excitation placement techniques are developed using normal mode indicator functions, and the concept of modal controllability and observability along with genetic algorithms. The new and existing techniques are compared using three finite element models: the NASA eight-bay truss, the Jet Propulsion Laboratory Micro-Precision Interferometer test bed, and a car body.
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