Abstract
Reliability-based design optimization considers the uncertainties that lie in the designing process of resilient buildings and structures. To model uncertainty, the major challenge is to lower the high computational expense incurred by the double-loop approach, where the design optimization (outer loop) repeatedly calls the reliability analysis of each structural design (inner loop). An alternative is to convert the reliability constraints to deterministic constraints by using optimality conditions. Yet, the approximated results are often inaccurate when constraint functions are highly non-linear, non-continuous, or non-differentiable. To achieve better accuracy while attaining sufficient flexibility, the present study proposes a new framework to classify the structural designs into feasible/infeasible designs. The proposed framework is called SOS-ASVM by integrating the symbiotic organism search (SOS) and the active-learning support vector machine (ASVM). ASVM is adopted as the surrogate model, while SOS is used to seek more representative samples to improve the classification accuracy of ASVM. The SOS-ASVM was validated by comparisons with popular classification tools: conventional support vector machine, artificial neural network, and Kriging model. Three practical engineering cases are used to demonstrate the performance of the SOS-ASVM: a cantilever beam, a bracket structure, and a 25-bar space truss. The comparison results confirm the superiority of the proposed framework to other tools.