Abstract: 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.

Key words: surrogate model； adaptive sampling strategy； k-means algorithm； parallel； structural reliability