淡江大學機構典藏:Item 987654321/50456
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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/50456


    Title: Prediction of Flutter Derivatives by Artificial Neural Networks
    Authors: Chen, Chern-hwa;吳重成;Wu, Jong-cheng;Chen, Jow-hua
    Contributors: 淡江大學土木工程學系
    Keywords: Artificial neural network;Flutter derivative;Rectangular section model;Wind tunnel test
    Date: 2008-10
    Issue Date: 2010-08-09 17:56:42 (UTC+8)
    Publisher: Amsterdam: Elsevier BV
    Abstract: This study presents an approach using artificial neural networks (ANN) algorithm for predicting the flutter derivatives of rectangular section models without wind tunnel tests. Firstly, a database of flutter derivatives is identified from a back-propagation (BP) ANN model that is built using experimental dynamic responses of rectangular section models in smooth flow as the input/output data. Then, these limited sets of database are employed as input/output data to establish a prediction ANN frame model to further predict the flutter derivatives for other rectangular section models without conducting wind tunnel tests. The results presented indicate that this ANN prediction scheme works reasonably well. Therefore, instead of going through wind tunnel tests, this ANN approach provides a convenient and feasible option for expanding the flutter derivative database that can help to determine an appropriate basic shape of the bridge section in the preliminary design.
    Relation: Journal of Wind Engineering and Industrial Aerodynamics 96(10-11), pp.1925-1937
    DOI: 10.1016/j.jweia.2008.02.044
    Appears in Collections:[Graduate Institute & Department of Civil Engineering] Journal Article

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