A new sampling strategy for SVM-based response surface for structural reliability analysis

To evaluate failure probability of structures in the most general case is computationally demanding. The cost can be reduced by using the Response Surface Methodology, which builds a surrogate model of the target limit state function. In this paper authors consider a specific type of response surface, based on the Support Vector Method (SVM). Using the SVM the reliability problem is treated as a classification approach and extensive numerical experimentation has shown that each type of limit state can be adequately represented; however it could require a high number of sampling points. This work demonstrates that, by using a novel sampling strategy based on sampling directions, it is possible to obtain a good approximation of the limit state without high computational complexity. A second-order polynomial SVM model has been adopted, so the need of determining free parameters has been avoided. However, if needed, higher-order polynomial or Gaussian kernel can be adopted to approximate any kind of limit state. Some representative numerical examples show the accuracy and effectiveness of the presented procedure.