Full metadata record
|dc.contributor||Department of Civil and Structural Engineering||en_US|
|dc.creator||Howe, Wing-chi David||-|
|dc.publisher||Hong Kong Polytechnic University||-|
|dc.rights||All rights reserved||en_US|
|dc.title||Damage detection of structure based on neural network||en_US|
|dcterms.abstract||Civil infrastructures composed of buildings and bridges are aging in Hong Kong. The assessment of the structural damage has become an urgent problem to be resolved. The purpose of this dissertation is to present one of the reliable contributions in this field using Artificial Neural Networks (ANN) which is an efficient computing technique that has been widely used to solve complex problems in many fields. In this dissertation, neural network method is used for detecting structural damage location and extent for beam, frame and truss structures. Several comprehensive computer models were established in order to examine the usefulness of a variety of diagnostic parameters including natural frequencies, mode shape, and other parameters based on frequencies and mode shapes on damage detection. The required data are obtained through computer simulation by finite element analysis. The results from the neural network training have demonstrated that the parameter, modal strain energy change ratio (MSECR), provides high detectability in localization and quantification of structural damage for all type of structures. This parameter together with the neural networks developed is then applied to the damage detection of a steel plane frame in the laboratory with success.||en_US|
|dcterms.extent||xv, 323 leaves : ill. ; 30 cm||en_US|
|dcterms.LCSH||Structural failures -- China -- Hong Kong||en_US|
|dcterms.LCSH||Neural networks (Computer science) -- China -- Hong Kong||en_US|
|dcterms.LCSH||Hong Kong Polytechnic University -- Dissertations||en_US|
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|b15594592.pdf||For All Users (off-campus access for PolyU Staff & Students only)||15.99 MB||Adobe PDF||View/Open|
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