A Multiwavelet Neural Network‐Based Response Surface Method for Structural Reliability Analysis

Abstract

A new multiwavelet neural network-based response surface method is proposed for efficient structural reliability assessment. Although multiwavelet network can be used for approximating nonlinear functions, its application has been limited to small dimension problems due to computational cost. The new method expands the application of multiwavelet network to moderate dimension by introducing a series of intermediate nodes, and the number of these intermediate nodes is determined by the multiwavelet theory. Thus, a multidimensional function learning problem is transformed into a one-dimensional function learning problem. Four examples involving one stochastic finite element-based reliability problem illustrate the effectiveness of the proposed method, which indicate that the new method is more efficient up to 10 random variables than the classical multilayer perceptron-based response surface method.

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