Reliability assessment of engineering systems often requires repeated evaluations of limit-state functions that may rely on computationally expensive high-fidelity models, rendering direct sampling-based reliability analysis impractical. An effective solution is to approximate the limit-state function with a surrogate model that can be iteratively refined through active learning, thereby reducing the number of model evaluations. At each iteration, an acquisition strategy selects the next sample for evaluation by balancing two competing objectives: exploration, to reduce global predictive uncertainty, and exploitation, to improve accuracy near the failure boundary. Conventional strategies such as the U-function, EFF, ERF, REIF, and portfolio-based schemes encode this balance through single pointwise scores, concealing the underlying trade-off. In this work, we formulate sample acquisition as a multi-objective optimization (MOO) problem in which exploration and exploitation are explicit competing objectives, yielding a compact Pareto set that provides a quantifiable trade-off representation. To select samples from the Pareto set, we investigate principled MOO criteria and propose adaptive trade-off rules, including a scheduled exploration-to-exploitation shift and a reliability-aware selection rule. Across diverse limit-state functions, we evaluate all tested strategies through relative failure-probability error trajectories, sample-efficiency comparisons, and global rankings, showing that the adaptive MOO-based strategies achieve robust overall performance while consistently meeting strict error targets.

翻译：工程系统可靠性评估通常需要重复评估依赖于计算昂贵高保真模型的极限状态函数，这使得基于直接采样的可靠性分析难以实现。一种有效的解决方案是用代理模型近似极限状态函数，并通过主动学习迭代优化该模型，从而减少模型评估次数。在每次迭代中，采集策略通过平衡两个相互竞争的目标来选择下一个待评估样本：探索（降低全局预测不确定性）和利用（提高失效边界附近的精度）。传统策略（如U函数、EFF、ERF、REIF及组合基方案）通过单一逐点评分隐藏了潜在的权衡关系。本研究将样本采集问题构建为多目标优化（MOO）问题，其中探索和利用作为明确的竞争目标，生成可量化权衡表示的紧凑Pareto集。为从Pareto集中选取样本，我们研究了原则性MOO准则并提出了自适应权衡规则，包括计划性从探索到利用的转移策略和可靠性感知选择规则。通过在多个极限状态函数上采用相对失效概率误差轨迹、样本效率比较和全局排名对所有测试策略进行评估，结果表明基于自适应MOO的策略在持续满足严格误差目标的同时实现了稳健的整体性能。