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Study On The Structural Vibration Control And Damage Diagnosis Based On Intelligent Methods

Posted on:2005-10-06Degree:MasterType:Thesis
Country:ChinaCandidate:X Q HuangFull Text:PDF
GTID:2132360122987668Subject:Structural engineering
Abstract/Summary:PDF Full Text Request
It has been proved by theory and experiments that structuralvibration control, as a new technology is a valid method to reduce theseismic disasters. As one method in the structural control, semi-activecontrol becomes the developmental tendency of future structuralvibration control for it can approach the control effectiveness of activecontrol with only a little energy and it has the highest ratio ofeffectiveness to price. But because of the specialty and complexity ofcivil engineering, semi-active control is not perfect in theory andexperiment, and many control theories cannot be put in practice. Soconsidering practice, the research based on smart materials andintelligent methods brings vigor to the development of semi-activestructural control and its application. It solves the problems of thetraditional control theories and break new ground for the structuralcontrol methods. After the earthquake or other disasters, the structures would havesomewhat degree of damage. To determine the structure's damage degreeis very meaningful to take measures to protect the structure such asstructural reinforcing or vibration control. Based on the dynamicparameters measured through the non-damage detection technology–vibration diagnosis, one method which employs the highly effectiveoptimum-seeking ability of genetic algorithm to deduce the structuralphysical characteristic s and the other method that employs the excellentstudy ability of neural network to identify the location and degree of thecrack are presented. It has been proved by simulation examples that thesemethods are valid, feasible and accurate.
Keywords/Search Tags:displacement limit on-off control, time delay, active variable stiffness/damping( AVS/D)semi-active control, combining neural network predicting model, fuzzy neural network, genetic algorithm, structural damage diagnosis
PDF Full Text Request
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