Support vector regression based metamodel by sequential adaptive sampling for reliability analysis of structures

•Adaptive SVR model by sequential update of training data for reliability analysis.•Algorithm hinges on prediction accuracy of a metamodel near failure surface.•Two-stage adaptive scheme obtain necessary training data close to failure surface.•Successfully estimates reliability with different initial sample size.•The proposed adaptive approach with SVR outperforms over the other metamodels. Support vector regression (SVR) based metamodel is a powerful mean to alleviate computational challenge of Monte Carlo simulation (MCS) based reliability analysis of structure involving implicit limit state function. But, the sample size requirement is an important issue to achieve accuracy of estimated reliability. A two-stage iterative algorithm is explored to address this issue. The algorithm is hinged on the prediction accuracy of a metamodel near the failure surface region. In the first stage, an initial design of experiment is built by a space-filling design over the entire physical domain of the random variables. In the next stage, based on the prediction at MCS points using the previous SVR model, a subset of MCS samples are selected. These are now used to enrich existing design by adding more data points sequentially such that the new points are closer to the limit state and also as far as possible from the existing points. A comparative performance of reliability estimate by SVR with the proposed sequential adaptive approach and that of obtained by the relevance vector machines, Kriging and moving least square method based metamodels are performed to numerically demonstrate the improved reliability estimation capability of the proposed approach.