Prediction of Flutter Derivatives Using Artificial Neural Networks

[[abstract]]This paper develops an artificial neural network (ANN) algorithm to predict the flutter derivatives of rectangular section models. Firstly, the ANN model uses the experimental dynamic responses of the section model in smooth flow to train a back-propagation (BP) neural network frame. The flutter derivatives can be determined using weight matrices in the neural network. The second part of this study is to predict the flutter derivatives of the rectangular section models without wind tunnel tests. Based on the given flutter derivatives of the rectangular section models tested in wind tunnel, the prediction frames of neural network are then established. The flutter derivatives of the rectangular section models, with the B/D ratios other than those obtained from the wind tunnel tests can be predicted by using this approach. The results show that this prediction scheme is reasonably well. By using this ANN approach, the database of the aerodynamic coefficients of bridge sections could be expanded.[[conferencetype]]國際[[conferencedate]]20060716~20060719[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]Yokohama, Japa