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Optimum Design of Structures for Earthquake Induced Loading by Wavelet Neural Network

Optimum Design of Structures for Earthquake Induced Loading by Wavelet Neural Network

Eysa Salajegheh, Ali Heidari
Copyright: © 2007 |Pages: 21
ISBN13: 9781599040998|ISBN10: 1599040999|ISBN13 Softcover: 9781599041001|EISBN13: 9781599041018
DOI: 10.4018/978-1-59904-099-8.ch005
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MLA

Salajegheh, Eysa, and Ali Heidari. "Optimum Design of Structures for Earthquake Induced Loading by Wavelet Neural Network." Intelligent Computational Paradigms in Earthquake Engineering, edited by Nikos Lagaros and Yiannis Tsompanakis, IGI Global, 2007, pp. 80-100. https://doi.org/10.4018/978-1-59904-099-8.ch005

APA

Salajegheh, E. & Heidari, A. (2007). Optimum Design of Structures for Earthquake Induced Loading by Wavelet Neural Network. In N. Lagaros & Y. Tsompanakis (Eds.), Intelligent Computational Paradigms in Earthquake Engineering (pp. 80-100). IGI Global. https://doi.org/10.4018/978-1-59904-099-8.ch005

Chicago

Salajegheh, Eysa, and Ali Heidari. "Optimum Design of Structures for Earthquake Induced Loading by Wavelet Neural Network." In Intelligent Computational Paradigms in Earthquake Engineering, edited by Nikos Lagaros and Yiannis Tsompanakis, 80-100. Hershey, PA: IGI Global, 2007. https://doi.org/10.4018/978-1-59904-099-8.ch005

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Abstract

Optimum design of structures for earthquake induced loading is achieved by a modified genetic algorithm (MGA). Some features of the simulated annealing (SA) are used to control various parameters of the genetic algorithm (GA). To reduce the computational work, a fast wavelet transform is used. The record is decomposed into two parts. One part contains the low frequency of the record, and the other contains the high frequency of the record. The low-frequency content is used for dynamic analysis. Then using a wavelet neural network, the dynamic responses of the structures are approximated. By such approximation, the dynamic analysis of the structure becomes unnecessary in the process of optimisation. The wavelet neural networks have been employed as a general approximation tool for the time history dynamic analysis. A number of structures are designed for optimal weight and the results are compared to those corresponding to the exact dynamic analysis.

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