Using neural networks to model and predict amplitude dependent damping in buildings

Li, Q.S.; Liu, D.K.; Fang, J.Q.; Jeary, A.P.; Wong, C.K.

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자료유형: 학술저널

저자정보: Li, Q.S. (Department of Building and Construction, City University of Hong Kong) Liu, D.K. (Department of Building and Construction, City University of Hong Kong) Fang, J.Q. (Department of Building and Construction, City University of Hong Kong) Jeary, A.P. (Department of Building and Construction, City University of Hong Kong) Wong, C.K. (Department of Building and Construction, City University of Hong Kong)

저널정보: 테크노프레스 Wind & structures Wind & structures 제2권 제1호

발행연도: 1999.1

수록면: 25 - 40 (16page)

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In this paper, artificial neural networks, a new kind of intelligent method, are employed to model and predict amplitude dependent damping in buildings based on our full-scale measurements of buildings. The modelling method and procedure using neural networks to model the damping are studied. Comparative analysis of different neural network models of damping, which includes multi-layer perception network (MLP), recurrent neural network, and general regression neural network (GRNN), is performed and discussed in detail. The performances of the models are evaluated and discussed by tests and predictions including self-test, "one-lag" prediction and "multi-lag" prediction of the damping values at high amplitude levels. The established models of damping are used to predict the damping in the following three ways : (1) the model is established by part of the data measured from one building and is used to predict the another part of damping values which are always difficult to obtain from field measurements : the values at the high amplitude level. (2) The model is established by the damping data measured from one building and is used to predict the variation curve of damping for another building. And (3) the model is established by the data measured from more than one buildings and is used to predict the variation curve of damping for another building. The prediction results are discussed.

#full-scale measurement #amplitude dependent damping #artificial neural networks #general regression network #prediction

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