Asymptotic sampling is an efficient simulation-based technique for estimating small failure probabilities of structures. The concept of asymptotic sampling utilizes the asymptotic behavior of the reliability index with respect to the standard deviations of the random variables. In this method, the standard deviations of the random variables are progressively inflated using a scale parameter to obtain a set of scaled reliability indices. The collection of the standard deviation scale parameters and corresponding scaled reliability indices are called support points. Then, least square regression is performed using these support points to establish a relationship between the scale parameter and scaled reliability indices. Finally, an extrapolation is performed to estimate the actual reliability index. The accuracy and performance of the asymptotic sampling method are affected by various factors including the sampling method used, the values of the scale parameters, the number of support points, and the formulation of extrapolation models. The purpose of this study is to make a critical evaluation of the performance of the asymptotic sampling method for highly safe structures, and to provide some guidelines to improve the performance of asymptotic sampling method. A comprehensive numerical procedure is developed, and structural mechanics example problems with varying number of random variables and probability distribution types are used in assessment of the performance of asymptotic sampling method. It is found that generating the random variables by Sobol sequences and using the 6-model mean extrapolation formulation give slightly more accurate results. Besides, the optimum initial scale parameter is approximately around 0.3 and 0.4, and the optimum number of support points is typically four for all problems. As the reliability level increases, the optimum initial scale parameter value decreases, and the optimum number of support points increases.

渐近采样是一种有效的基于模拟的技术，用于估计结构的小故障概率。渐近抽样的概念利用了可靠性指标相对于随机变量标准差的渐近行为。在该方法中，随机变量的标准偏差使用尺度参数逐渐膨胀以获得一组缩放的可靠性指标。标准差尺度参数和相应的尺度可靠性指标的集合称为支持点。然后，使用这些支持点进行最小二乘回归，以建立尺度参数和尺度可靠性指标之间的关系。最后，进行外推以估计实际的可靠性指标。渐近采样方法的准确性和性能受多种因素影响，包括所使用的采样方法、尺度参数的值、支持点的数量以及外推模型的制定。本研究的目的是对高度安全结构的渐近采样方法的性能进行批判性评估，并为提高渐近采样方法的性能提供一些指导。开发了一个全面的数值程序，并使用具有不同数量的随机变量和概率分布类型的结构力学示例问题来评估渐近采样方法的性能。发现通过 Sobol 序列生成随机变量并使用 6 模型均值外推公式给出更准确的结果。此外，最佳初始尺度参数大约在 0.3 和 0.4 左右，对于所有问题，最佳支持点数通常为 4。随着可靠性水平的提高，最优初始尺度参数值减小，最优支撑点数增加。