面向结构可靠性分析的并行自适应加点策略

现有的自适应加点策略多局限于Kriging模型，或在每次迭代过程中只能选取一个最佳样本点，效率较低.为解决上述问题，本文提出了一种通用的并行自适应加点策略CF-K.该方法考虑了样本点的局部不确定性并确保所选样本点分布在极限状态函数附近；此外，结合k-means算法以实现并行计算，即利用多台计算机在每次迭代的同时进行多个样本的仿真.算例分析表明，与其他方法相比，所提方法在满足精度要求的条件下具有更少的迭代次数，更节省时间.基于所提方法的结构可靠性分析不仅在计算效率和精度之间取得了较好的平衡，在理论上还可用于任何现有的代理模型.

Parallel Adaptive Sampling Strategy for Structural Reliability Analysis

Many existing adaptive sampling strategies are limited to the Kriging models， or are with low efficiency for only selecting one best sample point at each iteration. To solve the above issues， a general parallel adaptive sampling strategy CF-K is proposed. The proposed method considers the local uncertainty of the sample points and ensures that the selected sample points distribute around the limit-state function. Furthermore， the k-means algorithm is incorporated to achieve parallel computation， which means that several sample points can be simulated simultaneously at each iteration on several computers. The numerical cases show that the proposed method has less iterations and saves more time than other methods under the condition of satisfying the precision. The structural reliability analysis based on the proposed method not only achieves a good balance between computational efficiency and accuracy， but also be available for any existing surrogate models in principle.