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An adaptive divergence-based method for structural reliability analysis via multiple Kriging models

机译：基于自适应散度的多种Kriging模型结构可靠度分析方法

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摘要

This paper presents a novel multiple-surrogate method to compute the probability of failure. Recently, some adaptive methods using Kriging surrogate model have been developed for structural reliability assessment. However, a suitable regression trend in the Kriging model is so important to reduce the number of calls to performance function by adaptive Kriging method. Hence, this paper develops the original adaptive Kriging method to use different regression trends. The proposed method is based on a machine-learning algorithm, namely “query by committee”, in which adaptive training samples are selected based on the maximal disagreement between multiple surrogate models. The proposed method has a low sensitivity to the type of regression trends, because the algorithm starts with different regression trends, and inappropriate regression trends are filtered out in the next iterations. Therefore, the use of multiple surrogates can provide an efficient tool to estimate the probability of failure. In addition, two new approaches of prediction with multiple surrogates are employed in the proposed method. These approaches are based on the local and global surrogate models that provided by the learning function. The performance of the proposed method is evaluated through three analytical and two structural problems. The results show the efficiency and accuracy of the proposed method.
机译：本文提出了一种新的多重替代方法来计算故障概率。最近，已经开发了一些使用克里格代理模型的自适应方法来进行结构可靠性评估。但是，在Kriging模型中合适的回归趋势对于减少通过自适应Kriging方法调用性能函数的次数非常重要。因此，本文开发了原始的自适应克里格方法来使用不同的回归趋势。所提出的方法基于一种机器学习算法，即“按委员会查询”，其中基于多个代理模型之间的最大差异来选择自适应训练样本。所提出的方法对回归趋势的类型具有较低的敏感性，因为该算法从不同的回归趋势开始，并且在下一次迭代中滤除了不合适的回归趋势。因此，使用多个代理可以提供一种有效的工具来估计失败的可能性。另外，在所提出的方法中采用了两种具有多个替代物的预测新方法。这些方法基于学习功能提供的局部和全局替代模型。通过三个分析问题和两个结构问题来评估所提出方法的性能。结果表明了该方法的有效性和准确性。

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