首页> 外文期刊>Computer Methods in Applied Mechanics and Engineering >Unified uncertainty analysis under probabilistic, evidence, fuzzy and interval uncertainties
【24h】

Unified uncertainty analysis under probabilistic, evidence, fuzzy and interval uncertainties

机译:概率,证据,模糊和区间不确定性下的统一不确定性分析

获取原文
获取原文并翻译 | 示例

摘要

The uncertainty analysis of structures generally involves uncertain parameters of different types. In order to derive predictions regarding uncertain structural responses, it is crucial to represent the uncertainty appropriately according to the underlying information available. This paper presents a unified framework for uncertainty analysis under probabilistic, evidence, fuzzy and interval uncertainties, by which the quantities with sufficient data, sparse data, and subjective information can be simultaneously considered. A Taylor expansion-based unified uncertainty analysis (T-UUA) method is first proposed for small uncertainty problems. By temporarily neglecting the evidence, fuzzy and interval variables, the probability-evidence-interval-fuzzy model is degraded into a random problem, in which the expectations and variances of responses can be obtained as functions in terms of evidence, interval and fuzzy uncertainties. Then, through dealing with the evidence variables, the previous expectations and variances are further expressed as a summation of functions in terms of fuzzy and interval variables with basic probability assignments (BPAs). The fuzziness is then discretized by using alpha-cut technique and thus the expectations and variances are further expressed as functions of only intervals. Afterwards, by reconsidering the interval uncertainties, the bounds of the expectations and variances are computed via combining Taylor expansion with interval arithmetic. In addition, a dimensional reduction (DR)/efficient global optimization (EGO)-based unified uncertainty analysis (DR/EGO-UUA) method is also presented to solve the large uncertainty problems. The framework of DR/EGO-UUA is similar as T-UUA. However, in DR/EGO-UUA, the second moments of responses are computed by DR integrations, and their upper and lower bounds are calculated by the EGO. Finally, three numerical examples are investigated to demonstrate the effectiveness of the proposed methods. (C) 2019 Published by Elsevier B.V.
机译:结构的不确定性分析通常涉及不同类型的不确定参数。为了获得有关不确定结构响应的预测,至关重要的是根据可用的基础信息适当地表示不确定性。本文提出了一个在概率,证据,模糊和区间不确定性下进行不确定性分析的统一框架,通过该框架可以同时考虑具有足够数据,稀疏数据和主观信息的数量。首先提出了基于泰勒展开的统一不确定性分析(T-UUA)方法来解决小不确定性问题。通过暂时忽略证据,模糊和区间变量,将概率-证据-区间-模糊模型降级为一个随机问题,其中可以根据证据,区间和模糊不确定性获得响应的期望和方差作为函数。然后,通过处理证据变量,将先前的期望和方差进一步表示为具有基本概率分配(BPA)的模糊变量和区间变量的函数总和。然后使用阿尔法切分技术将模糊度离散化,因此期望和方差进一步表示为仅间隔的函数。然后,通过重新考虑区间不确定性,通过将泰勒展开与区间算术相结合来计算期望和方差的界限。此外,还提出了基于降维/高效全局优化(EGO)的统一不确定性分析(DR / EGO-UUA)方法来解决较大的不确定性问题。 DR / EGO-UUA的框架与T-UUA相似。但是,在DR / EGO-UUA中,响应的第二矩是通过DR积分计算的,其上限和下限是由EGO计算的。最后,研究了三个数值示例,以证明所提方法的有效性。 (C)2019由Elsevier B.V.发布

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号