SUPPORT VECTOR CLASSIFICATION FOR STRUCTURAL NON-PROBABILISTIC RELIABILITY ANALYSIS

The classification technology of support vector machine (SVM) was introduced to analyze the non-probabilistic reliability of structures with implicit limit state function. Based on the fragment description model of unascertained information, the training data sampling method was proposed. The sample data in basic variable range was transformed to those in normal interval variable scale, and the dimensions of training samples were unified. So the stability of SVM could be guaranteed and Monte Carlo simulation (MCS) became easier to perform. The problem of structural non-probabilistic reliability with implicit limit state function was solved. The accuracy and feasibility of this methodology were proved through two given examples.