目前，估算核电厂安全壳内压（internal pressure, IP）易损性和概率安全评估（probabilistic safety assessment, PSA）的方法常常忽视不确定性传播带来的不确定性量化。另外，传统的工程需求参数大多集中在局部响应指数上，未能充分考虑核安全壳的整体损伤演化。为了克服这些局限性，提出了一种可解释性机器学习（machine learning, ML）方法，该方法即使在样本量有限的情况下，也能充分反映整体损伤数据的非线性特性。研究结果表明，基于可解释性ML的框架不仅提高了分析的准确性，还有效地量化了预测的不确定性，超越了传统的线性不确定性量化方法。基于ML的方法更好地反映了内压与结构损伤之间的关系，同时为核安全壳的内压易损性和PSA结果提供了更可靠的置信区间。

Currently, methods for estimating the pressure-induced vulnerability of nuclear power plant containments and conducting probabilistic safety assessment (PSA) often overlook the uncertainty quantification arising from the propagation of inherent uncertainties. Additionally, traditional engineering demand parameters predominantly focus on local response indices, failing to adequately consider the overall damage evolution of the nuclear containment structure. To overcome these limitations, an interpretable machine learning (ML) approach was proposed to adequately capture the nonlinear features of the overall damage data even with limited sample size. The research findings demonstrate that the framework based on interpretable ML not only enhances the accuracy of the analysis but also effectively quantifies the prediction uncertainties, surpassing traditional linear uncertainty quantification methods. The ML-based method better reflects the relationship between internal pressure and structural damage, while providing more reliable confidence intervals for the pressure-induced vulnerability of nuclear containments and PSA results.