Nonparametric identification of a building structure from experimental data using wavelet neural network

Shih-Lin Hung*, Chiung-Shiann Huang, C. M. Wen, Y. C. Hsu

*此作品的通信作者

研究成果: Article同行評審

69 引文 斯高帕斯(Scopus)

摘要

This study presents a wavelet neural network-based approach to dynamically identifying and modeling a building structure. By combining wavelet decomposition and artificial neural networks (ANN), wavelet neural networks (WNN) are used for solving chaotic signal processing. The basic operations and training method of wavelet neural networks are briefly introduced, since these networks can approximate universal functions. The feasibility of structural behavior modeling and the possibility of structural health monitoring using wavelet neural networks are investigated. The practical application of a wavelet neural network to the structural dynamic modeling of a building frame in shaking tests is considered in an example. Structural acceleration responses under various levels of the strength of the Kobe earthquake were used to train and then test the WNNs. The results reveal that the WNNs not only identify the structural dynamic model, but also can be applied to monitor the health condition of a building structure under strong external excitation.

原文American English
頁(從 - 到)356-368
頁數13
期刊Computer-Aided Civil and Infrastructure Engineering
18
發行號5
DOIs
出版狀態Published - 1 1月 2003

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