The time-dependent failure credibility (TDFC) can reasonably measure the safety level of the time-dependent structure under the fuzzy uncertainty, but the direct optimization algorithm to estimate the TDFC requires large computational cost and even results in locally optimal solutions. Therefore, an efficient method is proposed for estimating the TDFC by combining the fuzzy simulation and the single-loop Kriging model. In the proposed method, fuzzy inverse transformation theorem is firstly used to transform the estimation of the TDFC into a sample classification problem, in which the candidate sample pool generated by fuzzy simulation (FS) is classified into the failure group and the safety one. For improving the efficiency of the classification, a Kriging model is adaptively trained by an elaborate U-learning function in the candidate sample pool. After the candidate sample is divided into the failure group and the safety one by the convergent Kriging model, the TDFC can be estimated as a byproduct easily. The innovation of the proposed method includes two aspects: establishing the idea of the fuzzy simulation combined with the single-loop Kriging model to estimate TDFC efficiently and robustly, and designing an elaborate U-learning function to improve the efficiency of training the single-loop Kriging model. The presented examples validate the efficiency of the proposed method under the acceptable precision.

基于时间的失效可信度（TDFC）可以合理地测量模糊不确定性下基于时间的结构的安全水平，但是直接优化算法估计TDFC需要大量的计算成本，甚至会导致局部最优解。因此，提出了一种将模糊仿真与单环克里格模型相结合的估计TDFC的有效方法。在该方法中，首先利用模糊逆变换定理将TDFC的估计转化为样本分类问题，将模糊模拟（FS）生成的候选样本库分为失效组和安全组。为了提高分类效率，通过候选样本池中精心设计的U学习功能对Kriging模型进行自适应训练。通过收敛的克里格模型将候选样本分为失效组和安全组后，可以轻松地将TDFC估计为副产品。该方法的创新包括两个方面：建立模糊仿真与单环克里格模型相结合的思想，以有效，鲁棒地估计TDFC；设计精细的U学习函数，以提高单环训练的效率。克里格模型。给出的例子验证了所提方法在可接受的精度下的效率。建立模糊仿真思想，结合单环克里格模型有效，鲁棒地估计TDFC，并设计精细的U学习函数，提高训练单环克里格模型的效率。给出的例子验证了所提方法在可接受的精度下的效率。建立模糊仿真思想，结合单环克里格模型有效，鲁棒地估计TDFC，并设计精细的U学习函数，提高训练单环克里格模型的效率。给出的例子验证了所提方法在可接受的精度下的效率。