Efficient reliability analysis based on adaptive sequential sampling design and cross-validation
Efficient reliability analysis based on adaptive sequential sampling design and cross-validation
复制标题
基于自适应顺序采样设计和交叉验证的高效可靠性分析
DOI：
10.1016/j.apm.2018.02.012
复制
发表时间：
2018
影响因子：
5
通讯作者：
Wei Guo
中科院分区：
文献类型：
--
作者：
Ning-Cong Xiao;Ming J. Zuo;Wei Guo
代理模型已被证明是一种有效的策略，结构系统的昂贵的评估模拟和结构可靠性分析是非常有用的。近年来，许多基于克里金模型的自适应序贯抽样方法被发展起来用于有效的可靠性分析。在本文中，一个新的学习函数的基础上交叉验证的指导思想，提出了自适应地选择新的训练点在每次迭代的可靠性分析。新的学习函数同时考虑了代理模型的认知不确定性和随机变量的偶然不确定性的影响。使用所提出的新学习函数可以实现三个目标，即，大多数所选择的新训练样本点（1）是从期望区域中选择的，以提高计算效率;（2）位于极限状态函数周围和具有高可靠性灵敏度的区域中;以及（3）倾向于远离当前设计中的现有训练点，以避免聚类问题。所提出的学习函数部分地与故障概率有关。所提出的方法易于编码和理解以及实现。最后通过五个算例验证了所提方法的准确性、有效性和适用性。
Surrogate-models have proven to be an effective strategy for structural systems with expensive-to-evaluate simulations and are very useful for structural reliability analysis. Many kriging model based adaptive sequential sampling methods have been developed recently for efficient reliability analysis. In this paper, a new learning function based on cross-validation is proposed as the guideline to adaptively select new training points at each iteration for reliability analysis. The epistemic uncertainty of the surrogate models and the effects of the aleatory uncertainty of the random variables are considered simultaneously in the proposed new learning function. Three goals can be achieved using the proposed new learning function, i.e., most of the selected new training sample points (1) are selected from the desired regions to improve computational efficiency; (2) reside around the limit-sate functions and in the regions with high reliability sensitivity; and (3) tend to be far away from existing training points in the current design to avoid the clustering problem. The proposed learning function is partly linked to the probability of failure. The proposed method is easy to code and understand as well as implement. Five numerical examples are finally used to validate the accuracy and efficiency as well as applicability of the proposed method.