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Volume 39 Issue 11
Nov.  2013
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Journal of Beijing University of Aeronautics and Astronautics  > 2013  >  39(11): 1460-1463.
Meng Guangwei, Li Guangbo, Li Feng, et al. Structural reliability analysis based on polynomial basis function neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2013, 39(11): 1460-1463. (in Chinese)
Citation: Meng Guangwei, Li Guangbo, Li Feng, et al. Structural reliability analysis based on polynomial basis function neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2013, 39(11): 1460-1463. (in Chinese)
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Structural reliability analysis based on polynomial basis function neural network

  • College of Mechanical Science and Engineering, Jilin University, Changchun 130025, China

  • Received Date: 20 Mar 2013
  • Publish Date: 30 Nov 2013
    Abstract
  • The polynomial basis function neural network method for structural reliability calculation was adopted. When the limit state function of the structure is highly complex or with nonlinearity, sometimes, limit state function is implicit, the traditional method of structure reliability analysis calculation is time-consuming. The polynomial basis function neural network method provides an effective means for the reliability analysis of large scale structures. Based on the polynomial approximation theory, a pseudo-inverse of random variable input matrix was used to be incentive function to determine the weights between the hidden layer and output layer with the approximation of neural network. The failure probability was calculated by first-order reliability method (FORM). The numerical example results show the proposed method has ability of solving the structural reliability analysis problems. The test results show that the formula of the proposed method is simple and easy to program. A new method for solving the structural reliability analysis was displayed.

     

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