Evidence Theory and Differential Evolution for Uncertainty Quantification of Structures
Evidence Theory and Differential Evolution for Uncertainty Quantification of Structures
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DOI：
10.4028/www.scientific.net/amm.249-250.1112
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发表时间：
2012-12
期刊：
影响因子：
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通讯作者：
L. Deng;He-sheng Tang;C. Hu;S. Xue
中科院分区：
文献类型：
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作者：
L. Deng;He-sheng Tang;C. Hu;S. Xue
由于建模、仿真、测量和可靠性评估以及设计优化过程中缺乏知识或信息不完整、不准确、不明确，仅用一种框架（概率论）来量化系统中的不确定性，由于数据或知识的不精确性，存在局限性。本文提出了一种新的证据理论来代替经典的概率论来处理数据不精确的情况。为了降低基于证据理论不确定量化分析的计算成本，提出了基于微分演化的区间优化算法来计算边界值.以典型桁架结构的偶然不确定和认知不确定问题为例验证所提出方法的准确性和有效性.
Due to lack of knowledge or incomplete, inaccurate, unclear information in the modeling, simulation, measurement and reliability assessment and design optimization, there are limitations in using only one framework (probability theory) to quantify the uncertainty in a system because of the impreciseness of data or knowledge. In this paper, evidence theory is proposed as an alternative to the classical probability theory to handle the imprecise data situation. In order to alleviate the computational difficulties in the evidence theory based uncertainty quantification (UQ) analysis, a differential evolution based interval optimization for computing bounds method is developed. A typical truss structure with the aleatory and epistemic uncertainties is investigated to demonstrate accuracy and efficiency of the proposed method.