Estimating Distribution of Structural Responses based on Cubic Normal Distribution and Artificial Neural Network

Abstract

The approximation of the probability density function (PDF) of the structural response is importance in structural reliability analysis. The recently developed cubic normal distribution can be used to represent the PDF of structural response due to its high flexibility and its large applicable range, whose parameters are evaluated by its first four moments. Therefore, efficient estimation of the moments is of great importance. Although some methods have been developed, evaluating the moments of structural response from the sight of balancing accuracy and efficiency remains a challenge, especially when the structural response is implicit and high-dimensional. In this paper, based on the artificial neural network (ANN), a new method is developed to efficiently estimate the statistical moments of structural response. The main procedure of the proposed method includes two steps: the structural response is approximated by the ANN and then the moments of structural response can be easily obtained. A RC frame structure with non-linear behavior is used to demonstrate the efficiency, accuracy, and applicability of the proposed method. The results show that the proposed method is of high accuracy and efficiency and provides a robust tool for representing the PDF of structural response.