The probability of fracture (POF) assessment of complex cracked structures is a difficult task in the reliability assessment of engineering structures. Due to the complexity of structure, balancing efficiency and accuracy is the top concern in POF calculation. In this study, a novel probabilistic solution method called AKNN-MCS (Active learning-based K-Nearest Neighbors-Monte Carlo Simulation) is proposed. Combining the active learning strategy and the KNN algorithm, this method could get accurate POF results using a few samples. In detail, POF calculation is treated as a classification problem. A learning function is proposed to select sample points near the limit state surface. Then the selected sample points are added into training data set T. A convergence criterion is defined to decide when to stop the enrichment of T. Thanks to the above active learning strategy, the trained KNN model could have a great generalization ability with only a few training samples required. The proposed method is validated by POF assessment of a finite thickness plate containing a surface semi-elliptical crack and POF assessment of the CT specimen. Results show that AKNN-MCS is three or four orders of magnitude more efficient than MCS for almost identical POF results.

复杂裂纹结构的断裂概率（POF）评估是工程结构可靠性评估中的一项艰巨任务。由于结构的复杂性，平衡效率和准确性是 POF 计算的首要问题。在这项研究中，一种新颖的概率溶液方法称为AKNN-MCS（甲莫如为基础的学习ķ - ñ earest Ñ eighbors -M onte Ç阿洛小号模拟）提出。结合主动学习策略和KNN算法，该方法可以使用少量样本获得准确的POF结果。详细地，POF 计算被视为分类问题。提出了一种学习函数来选择极限状态表面附近的样本点。然后将选定的样本点添加到训练数据集T 中。定义收敛标准来决定何时停止T的富集. 得益于上述主动学习策略，训练后的 KNN 模型只需少量训练样本即可具有很强的泛化能力。所提出的方法通过包含表面半椭圆裂纹的有限厚度板的 POF 评估和 CT 试样的 POF 评估得到验证。结果表明，对于几乎相同的 POF 结果，AKNN-MCS 比 MCS 效率高三到四个数量级。