Adaptive stochastic configuration network ensemble for structural reliability analysis

This paper develops a new adaptive surrogate model method for structural reliability analysis, which is constructed by approximating the limit state function (LSF) using stochastic configuration network (SCN) ensemble model. The proposed ensemble model integrates three SCN base models and sets weights for each prediction result, reducing outliers and greatly improving the output accuracy and stability of the algorithm. In constructing a surrogate model for reliability analysis, a new adaptive sampling strategy is designed, including a novel learning function. The new strategy enables the most informative sample points are selected and significantly reduce the number of required sample point. Without further calls to the LSF, the structural failure probability is quickly evaluated using Monte Carlo simulation in terms of the constructed SCN ensemble surrogate model. Three benchmark problems, including a highly nonlinear problem with small failure probability and two multivariable engineering structural problems, are used to verify the effectiveness of the proposed method.