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Polymorphic uncertainty quantification for engineering structures via a hyperplane modelling technique

delete2022-08-01
delete19
PRE
AI
Q
Qihan Wang
Y
Yuan Feng
吴迪 封面图
吴迪 (Di Wu) *
G
Guoyin Li
刘
刘振宇 (Zhenyu Liu)
W
Wei Gao *
DOI:10.1016/j.cma.2022.115250delete
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摘要

摘要

En 中文
This paper proposes a hyperplane modelling technique aided polymorphic uncertainty quantification strategy for various engineering applications. Accumulative experiences from industrial applications have continuously revealed that probability distribution characteristics for system properties cannot always be precisely determined, due to the scarcity of available information. Thus, the polymorphic uncertainty is introduced to consider fuzzy and random uncertainty simultaneously. To tackle such uncertainty quantification challenges, Multiple Target Strategy (MTS) and Single Target Strategy (STS) are proposed. MTS is a generalized polymorphic uncertainty quantification strategy, capable of providing sufficient amounts of the statistical information of the concerned structural response. On the contrary, STS considers the polymorphic uncertainty quantification in a more precise and efficient manner. Both strategies are implemented based on their respective hyperplane models. For hyperplane model construction, a newly developed ensemble meta-regression technique, namely AdaBoost Extended Support Vector Regression (Ada-X-SVR), is embedded with a novel kernel function. The proposed hyperplane modelling aided polymorphic uncertainty quantification framework provides the feasibility of both generalized and targeted estimations, autolearning and information update features. Furthermore, to demonstrate the applicability and computational efficiency of the proposed strategies, a benchmark analysis with an available analytical solution and three engineering applications with linear and nonlinear performance are fully investigated. (c) 2022 Elsevier B.V. All rights reserved.
Keyword:
Hyperplane construction technique
AdaBoost Extended Support Vector Regression (ada-X-SVR)
Polymorphic uncertainty quantification
Machine learning
Engineering application

期刊

Computer Methods in Applied Mechanics and Engineering 封面图
Computer Methods in Applied Mechanics and Engineering
IF:
7.3
论文数:
1.3W
被引数:
5.6W

机构

U
university of technology sydney
学者数:
1.6W
论文数: 2.0W
被引数: 25
Z
zhejiang university
学者数:
17.7W
论文数: 12.1W
被引数: 152
引用论文

引用论文

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err1998-08-01
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PREAI
errGiuseppe Borzellino; Giovanni De Manzoni; Francesco Ricci
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