Surrogate model-based reliability analysis for structural systems with correlated distribution parameters
Surrogate model-based reliability analysis for structural systems with correlated distribution parameters
复制标题
具有相关分布参数的结构系统基于替代模型的可靠性分析
DOI：
10.1007/s00158-020-02505-7
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发表时间：
2020-02
影响因子：
3.9
通讯作者：
Hu Wan
中科院分区：
文献类型：
--
作者：
Ning-Cong Xiao;Kai Yuan;Zhangchun Tang;Hu Wan
不确定性通常由随机变量建模，分布参数的值由所收集的样本估计。在实际工程中，分布参数的估计可能存在点样本和区间样本，那么它们的值就是区间而不是点值。鉴于所有分布参数都是从同一组样本中估计的，因此它们必须相关，而不是相互独立。在这项研究中，区间分布参数之间的相关性被认为是使用椭圆模型建模，并首次提出了基于蒙特卡罗模拟（MCS）的相关分布参数的可靠性方法，表示为D-MCS。针对真实的应用中性能函数为隐式函数，计算量大的问题，提出了一种基于自适应代理模型的区间分布参数相关结构系统可靠性分析方法。在U函数的基础上，提出了一种新的有效的学习函数，在每次迭代中自适应地添加最好的新训练样本。给出了相应的终止准则。基于最终构建的代理模型计算失效概率的上下界。与传统的独立性假设可靠度方法相比，该方法能提供更精确的可靠度结果，并可用于混合变量结构体系的可靠度分析。所提出的方法易于编码和理解。三个数值例子的研究表明，所提出的方法的适用性。
Uncertainties are usually modeled by random variables, and the values of distribution parameters are estimated from the collected samples. In practical engineering, point and interval samples are possibly available for the estimation of distribution parameters; then, their values are intervals instead of point values. In view of the fact that all distribution parameters are estimated from the same set of samples, they must be correlated rather than mutually independent. In this study, the correlation among interval distribution parameters is considered and modeled using ellipse models, and the Monte Carlo simulation (MCS)-based reliability method for correlated distribution parameters, denoted as D–MCS, is first proposed. Performance functions are usually implicit functions involving simulation that are expensive-to-evaluate evaluate in real applications; hence, an efficient adaptive surrogate model-based reliability method for structural systems with correlated interval distribution parameters is proposed to reduce computational burden. A new and efficient learning function based on the U function is developed to adaptively add the best new training samples at each iteration. The corresponding stopping criterion to terminate the proposed algorithm is also developed. The lower and upper bounds of probability of failure are calculated based on the final constructed surrogate model. The proposed method is effective because it can provide more accurate reliability results compared with traditional independence assumption reliability methods, and it can be used for structural systems with mixed variables. The proposed method is easy to code and understand. Three numerical examples are investigated to show the applicability of the proposed method.
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影响因子：
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作者：
L. P. Zhu;I. Elishakoff;J. Starnes
通讯作者：
L. P. Zhu;I. Elishakoff;J. Starnes
考虑强度退化的非线性疲劳损伤累积模型及其在疲劳可靠性分析中的应用
影响因子：
4.2
作者：
Haiqing Li;Hong-Zhong Huang;Shun-Peng Zhu;Huiying Gao
通讯作者：
Huiying Gao
影响因子：
3.9
作者：
C. Jiang;J. Zheng;X. Han-
通讯作者：
C. Jiang;J. Zheng;X. Han-
影响因子：
5.8
作者：
G. Muscolino;R. Santoro;A. Sofi
通讯作者：
G. Muscolino;R. Santoro;A. Sofi
DOI：
10.1016/j.cma.2019.06.036
发表时间：
2019-10
影响因子：
7.2
作者：
Chong Wang;H. Matthies
通讯作者：
Chong Wang;H. Matthies