Data-based polyhedron model for optimization of engineering structures involving uncertainties

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE DataCentric Engineering Pub Date : 2021-06-29 DOI:10.1017/dce.2021.8
Z. Qiu, Han Wu, I. Elishakoff, Dongliang Liu
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引用次数: 2

Abstract

Abstract This paper studies the data-based polyhedron model and its application in uncertain linear optimization of engineering structures, especially in the absence of information either on probabilistic properties or about membership functions in the fussy sets-based approach, in which situation it is more appropriate to quantify the uncertainties by convex polyhedra. Firstly, we introduce the uncertainty quantification method of the convex polyhedron approach and the model modification method by Chebyshev inequality. Secondly, the characteristics of the optimal solution of convex polyhedron linear programming are investigated. Then the vertex solution of convex polyhedron linear programming is presented and proven. Next, the application of convex polyhedron linear programming in the static load-bearing capacity problem is introduced. Finally, the effectiveness of the vertex solution is verified by an example of the plane truss bearing problem, and the efficiency is verified by a load-bearing problem of stiffened composite plates.
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基于数据的多面体模型在不确定性工程结构优化中的应用
摘要本文研究了基于数据的多面体模型及其在工程结构不确定线性优化中的应用,特别是在基于模糊集的方法中缺乏概率性质或隶属函数信息的情况下,用凸多面体来量化不确定性更为合适。首先,我们介绍了凸多面体方法的不确定性量化方法和切比雪夫不等式的模型修正方法。其次,研究了凸多面体线性规划最优解的性质。然后给出并证明了凸多面体线性规划的顶点解。其次,介绍了凸多面体线性规划在静力承载力问题中的应用。最后,通过一个平面特拉斯承载问题的实例验证了顶点解的有效性,并通过一个加筋复合板的承载问题验证了其有效性。
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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