The metamodel-assisted reliability method opens a promising way to achieve efficient structural reliability assessment for structures with expensive-to-evaluate simulations. The advances in machine learning promote the development of the metamodel technique over the last decades. In this study, an active learning reliability method is presented by the combination of the extreme learning machine(ELM) and an efficient sequential sampling method with the framework of the Bayesian optimization theory. To determine the hyperparameters of ELM automatically, an adaptive extreme learning machine is introduced to approximate the performance function for reliability analysis. Furthermore, a novel active learning function inspired by the ensemble learning strategy is established to select the next best sample for approximation model refinement. Correspondingly, an effective stopping criterion on the cross-validation technique is built to terminate the active learning process timely. Four problems including numerical examples and practical engineering structures are analyzed. The test results show that the proposed method provides a satisfactory failure probability estimation with fewer performance function evaluations for these different reliability problems.

元模型辅助可靠性方法开辟了一条有前途的方法，可以通过昂贵的评估模拟实现结构的有效结构可靠性评估。在过去的几十年中，机器学习的进步促进了元模型技术的发展。本研究在贝叶斯优化理论的框架下，将极限学习机（ELM）与高效的顺序采样方法相结合，提出了一种主动学习可靠性方法。为了自动确定 ELM 的超参数，引入了自适应极限学习机来逼近可靠性分析的性能函数。此外，建立了受集成学习策略启发的新型主动学习函数，以选择下一个最佳样本进行近似模型细化。相应地，在交叉验证技术上建立了一个有效的停止标准，以及时终止主动学习过程。分析了包括数值例子和实际工程结构在内的四个问题。测试结果表明，对于这些不同的可靠性问题，所提出的方法以较少的性能函数评估提供了令人满意的失效概率估计。