Structural Reliability Analysis Using Sequential Surrogate Models

Performance functions of components and structural systems are often given using implicit functions for many practical engineering. An efficient surrogate-models-based reliability analysis method is proposed in this paper, a new sample selection learning function is constructed as a guideline to adaptively select new sample point at each iteration. The proposed learning function considers the weights of variables and ensures that the selected sample points reside not only around the limit-state functions, but also far away each other. The numerical examples show that the proposed method is accuracy and robustness, thus it can be used for structural systems with implicit performance function and various existing surrogate models (e.g., neural networks, support vector machine, response surface model). The proposed method provides a novel method for structural reliability analysis.