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A new measurement for structural uncertainty propagation based on pseudo-probability distribution

机译：基于伪概率分布的结构不确定性传播的新度量

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摘要

Uncertainty propagation (UP) offers a powerful tool for describing uncertainties from input parameters to output responses in a system. The existing methods of non-probabilistic UP can only evaluate the upper and lower bounds of structural responses. In this study, a new non-probabilistic UP method is proposed, attempting to provide more detailed quantification of uncertain responses between the lower and upper bounds. A concept of pseudo probability distribution is proposed under the non-probabilistic UP frame to quantify the possibilities of system responses. The uncertainties of structural parameters are modeled as a multi-dimensional ellipsoid convex set in the proposed UP method. The ellipsoid domain is divided into two parts using the first-order approximation of the system-state function. Then the volume ratio of the divided domain and the whole ellipsoid domain can be used to calculate the pseudo-probability of system responses. The sequential improved Hasofer-Lind-Rackwitz-Fiessler (iHL-RF) algorithm is adopted to effectively obtain the most probable expansive point of system-state function. The proposed UP method can not only provide accurate response bounds, but also objectively quantify the relatively accurate possibilities of each response value. In the numerical examples, the proposed UP method is compared with the Monte Carlo simulation method and traditional non-probabilistic uncertainty propagation method, and the calculated results demonstrate the validity and effectiveness of the proposed UP method. (C) 2018 Elsevier Inc. All rights reserved.
机译：不确定性传播（UP）提供了一个强大的工具，用于描述系统中从输入参数到输出响应的不确定性。现有的非概率UP方法只能评估结构响应的上限和下限。在这项研究中，提出了一种新的非概率UP方法，试图提供下限和上限之间不确定响应的更详细量化。在非概率UP框架下提出了伪概率分布的概念，以量化系统响应的可能性。提出的UP方法将结构参数的不确定性建模为多维椭球凸集。使用系统状态函数的一阶近似将椭球域分为两部分。然后，可将划分域与整个椭球域的体积比用于计算系统响应的伪概率。采用顺序改进的Hasofer-Lind-Rackwitz-Fiessler（iHL-RF）算法来有效地获得系统状态函数的最可能扩展点。提出的UP方法不仅可以提供准确的响应范围，而且可以客观地量化每个响应值的相对准确的可能性。在数值例子中，将所提出的UP方法与蒙特卡罗模拟方法和传统的非概率不确定性传播方法进行了比较，计算结果证明了所提出的UP方法的有效性和有效性。 （C）2018 Elsevier Inc.保留所有权利。

著录项

  • 来源
    《Applied Mathematical Modelling》 |2018年第11期|744-760|共17页
  • 作者

    Liu Jie; Liu Hao; Jiang Chao; Han Xu; Zhang De Quan; Hu Yi Feng;

  • 作者单位

    Hunan Univ, Coll Mech & Vehicle Engn, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Hunan, Peoples R China;

    Hunan Univ, Coll Mech & Vehicle Engn, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Hunan, Peoples R China;

    Hunan Univ, Coll Mech & Vehicle Engn, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Hunan, Peoples R China;

    Hunan Univ, Coll Mech & Vehicle Engn, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Hunan, Peoples R China;

    Hebei Univ Technol, Sch Mech Engn, State Key Lab Reliabil & Intelligence Elect Equip, Tianjin 300401, Peoples R China;

    Hunan Univ, Coll Mech & Vehicle Engn, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Hunan, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Uncertainty propagation; Ellipsoidal convex model; Pseudo-probability distribution function; Volume ratio; Non-probabilistic uncertainty;

    机译：不确定性传播椭球凸模型伪概率分布函数体积比非概率不确定性;

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