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Wind and Structures   Volume 2, Number 1, March 1999, pages 25-40
DOI: https://doi.org/10.12989/was.1999.2.1.025
 
Using neural networks to model and predict amplitude dependent damping in buildings
Q. S. Li, D. K. Liu, J. Q. Fang, A. P. Jeary and C. K. Wong

 
Abstract     [Buy Article]
    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 studies. 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,
 
Key Words
    full-scale measurement; amplitude dependent damping; artificial neural networks; general regression network; prediction.
 
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