Abstract
When the performance function cannot be expressed exactly, response surface method is often adopted for its clear thought and simple programming. The traditional method fits response surface with quadratic polynomials, and the accuracy can not be kept well, which only the area near checking point coincides well with the real limit state surface. In this paper, a new method based on global response surface of BP neural network is presented. In the present method, all the sample points for training network come from global area, and the real limit state surface can be fitted well in global area. Moreover, the examples and comparison are provided to show that the present method is much better than the traditional one, the amount of calculation of finite element analysis is reduced quite a lot, and the accuracy is increased.
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References
Bucher, C.-G., Bougund, U.: A Fast and Efficient Response Surface Approach for Structural Reliability Problem. Structural Safety 5(1), 57–66 (1990)
Faravelli, L.: A Response Surface Approach for Reliability Analysis. Journal of Engineering Mechanics 115(12), 2763–2781 (1989)
Parvin, A., Serpen, G.: Recurrent Neural Networks for Structural Optimization. Computer-Aided Civil and Infrastructure Engineering 14(6), 445–451 (1999)
Tashakori, A., Adeli, H.: Optimum Design of Cold-formed Steel Space Structures Using Neural Dynamics Mode. Journal of Structural Engineering 127 (2001)
Zhao, G.F.: The Reliability Principles and Applications for The Engineering Structure, pp. 145–158. Dalian University of Technology Press (1996) (in Chinese)
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© 2004 Springer-Verlag Berlin Heidelberg
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Gui, J., Sun, H., Kang, H. (2004). Structural Reliability Analysis via Global Response Surface Method of BP Neural Network. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_128
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DOI: https://doi.org/10.1007/978-3-540-28648-6_128
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22843-1
Online ISBN: 978-3-540-28648-6
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