Support Vector Classification Algorithm by Migration Sampling for Structural System Reliability Evaluation

In order to avoid the cumbersome bounding operation during system reliability evaluation procedures, a concept of system limit state surface was introduced on account of the characteristic of system multi-failure modes. Employing the support vector classification (SVC) algorithm to divide the security domain and failure domain, reconstitution of the system limit state surface could be achieved. On this basis, a migration sampling strategy using Latin hypercube sampling (LHS) was combined together with the SVC algorithm, finally leading to a new system reliability evaluation method of SVC migration sampling. Two widely used illustrative examples were introduced and analyzed by different methods for comparison. The results show that the SVC migration sampling algorithm possesses good sampling efficiency as well as superior convergence property. Compared with traditional Monte Carlo sampling methods, the algorithm presented can reduce sampling times by 87%with a relative error of less than 1%. The gross assumption of component failure state by β-unzipping method can also be avoided, which is more suitable for practical application to actual structure assessment.