Solving reliability-based design optimization (RBDO) by combining surrogate models is a powerful tool to deal with the output variation induced by uncertainties during actual engineering design. This paper aims to develop a strategy for solving RBDO problems by support vector regression (SVR) under the Bayesian inference, referred to as Bayesian SVR (BSVR). The BSVR model possesses the features of original SVR as well as providing the prediction variance to direct the sequential sampling process for probabilistic analysis in RBDO. In the meanwhile, a learning function combined with the sample pool truncation strategy is proposed to select the new training samples to adaptively update the BSVR model, which can accurately and efficiently approximate the important probabilistic constraint boundaries in the desired regions, and the newly selected training samples tend to be far away from the existing training points under the current design to avoid clustering. The computational capability and the good engineering applicability of the proposed method are verified by three numerical examples and two engineering applications about a stiffened rib of the wing edge and a latch-lock mechanism of the cabin hatch.

通过结合代理模型解决基于可靠性的设计优化 (RBDO) 是处理实际工程设计过程中不确定性引起的输出变化的有力工具。本文旨在开发一种在贝叶斯推理下通过支持向量回归 (SVR) 解决 RBDO 问题的策略，称为贝叶斯 SVR (BSVR)。BSVR 模型具有原始 SVR 的特征，并提供预测方差以指导 RBDO 中概率分析的顺序采样过程。同时，提出了结合样本池截断策略的学习函数来选择新的训练样本来自适应更新BSVR模型，可以准确有效地逼近所需区域的重要概率约束边界，并且新选择的训练样本在当前设计下倾向于远离现有的训练点以避免聚类。通过翼缘加筋肋和舱口门闩锁机构的三个数值算例和两个工程应用验证了所提方法的计算能力和良好的工程适用性。