Application of Artificial Neural Network to Structural Modal Analysis

• Main Authors: Hsin-Hwa Su, 蘇信華
• Other Authors: Chern-Hwa Chen
• Format: Others
• Language: zh-TW
• Published: 2001
• Online Access: http://ndltd.ncl.edu.tw/handle/83087168753919352282

碩士 === 中原大學 === 土木工程研究所 === 89 === Flutter derivatives are important aeroelastic parameters for representing the aeroelastic forces of a cable-supported bridge, and have employed to determine the critical wind velocity for flutter instability. The main purpose of this paper is to establish a procedure of identifying flutter derivatives for bridge deck by using artificial neural network, which has not been reported before. The artificial neural network consists of three layers, namely input layer, hidden layer, and output layer. The aeroelastic responses of the bridge under consideration are used to train the artificial neural network by using BP technique. Then, the weighting matrices in the network are used to determine the structural dynamic characteristics (e.g., frequencies, damping ratios) under wind loads or not. Finally, the flutter derivatives are determined from the identified dynamic characteristics. The accuracy and applicability of the proposed procedure are demonstrated by comparing the present results with those from other techniques in processing numerical simulation data and measured data of section model test. Besides, the proposed method can be applied to process the field test data of bridge (e.g. impact test and ambient test), the results are quite accurate compared with those from ARV identification technique.