A new radial basis function active learning method based on distance constraint for structural reliability analysis

Strongly nonlinear structural systems exhibit high computational errors when dependability is calculated using conventional approaches such as the primary second-order method of moments and the secondary second-order method of moments. The combination of the proxy model and Monte Carlo simulation is an effective method to solve the structural failure probability problem. However, existing studies on the active learning methods of proxy models for reliability calculation mainly focus on the kriging model, while for radial basis interpolation, the existing research results are relatively few. Based on the above analysis, this paper proposes to combine the cross-validation method with multiple kernel functions to evaluate the uncertainty at the prediction points. The mathematical expression of the active learning function considering three factors is proposed: the linear combination of the distance from the surface of limit state and the uncertainty of the predicted value of the proxy model as the optimization objective function, and the distance between the sample to be selected and the initial sample point as the constraint condition. Meanwhile, using the idea of the penalty function, the constrained problem is transformed into the unconstrained problem to get the final active learning function PLF. Finally, the efficiency, accuracy, and robustness of the PRBFM method are verified by classical cases and compared with other methods. It provides a new method and a new idea for the reliability analysis of complex structures.