Stochastic configuration network for structural reliability analysis

Abstract

Structural reliability analysis is of critical importance for ensuring the safety of many engineering systems. This paper proposes an efficient structural reliability analysis method based on stochastic configuration networks (SCNs), which is constructed by learning the real performance function of the structure. In this article, two kinds of innovative surrogate models of performance function are constructed based on the trained SCNs. To improve the approximation accuracy and efficiency of SCNs for complex high-dimensional performance functions, the improved high-dimensional model representation is applied to decompose the multivariate function into a combination of several low-dimensional functions. An innovative SCN-based learning strategy is proposed to simultaneously approximate all the low-dimensional component functions. By performing Monte Carlo Simulation on the established SCN-based surrogate models, we can successfully evaluate the failure probability of many complex multi-dimensional engineering structures. The proposed SCN-based reliability analysis method provides a powerful tool for estimating the failure probability of complex multi-dimensional engineering structures. Effectiveness of the proposed method in terms of accuracy and efficiency as well as stability is demonstrated through several numerical experiments.

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