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题名:
LIF: A new Kriging based learning function and its application to structural reliability analysis
作者: Sun ZL(孙志礼); Wang J(王健); Li R(李睿); Tong C(佟操)
作者部门: 空间自动化技术研究室
通讯作者: 王健
关键词: Structural reliability ; Kriging meta-model ; Learning function ; Design of experiment ; Least improvement function
刊名: RELIABILITY ENGINEERING & SYSTEM SAFETY
ISSN号: 0951-8320
出版日期: 2017
卷号: 157, 页码:152-165
收录类别: SCI ; EI
产权排序: 2
项目资助者: National Science and Technology Major Project of China [2013ZX04011-011] ; Fundamental Research Funds for the Central Universities of China [N140306004]
摘要: The main task of structural reliability analysis is to estimate failure probability of a studied structure taking randomness of input variables into account. To consider structural behavior practically, numerical models become more and more complicated and time-consuming, which increases the difficulty of reliability analysis. Therefore, sequential strategies of design of experiment (DoE) are raised. In this research, a new learning function, named least improvement function (LIF), is proposed to update DoE of Kriging based reliability analysis method. LIF values how much the accuracy of estimated failure probability will be improved if adding a given point into DoE. It takes both statistical information provided by the Kriging model and the joint probability density function of input variables into account, which is the most important difference from the existing learning functions. Maximum point of LIF is approximately determined with Markov Chain Monte Carlo(MCMC) simulation. A new reliability analysis method is developed based on the Kriging model, in which LIF, MCMC and Monte Carlo(MC) simulation are employed. Three examples are analyzed. Results show that LIF and the new method proposed in this research are very efficient when dealing with nonlinear performance function, small probability, complicated limit state and engineering problems with high dimension. (C) 2016 Elsevier Ltd. All rights reserved.
语种: 英语
EI收录号: 20164703040936
WOS记录号: WOS:000387195700014
WOS标题词: Science & Technology ; Technology
类目[WOS]: Engineering, Industrial ; Operations Research & Management Science
关键词[WOS]: RESPONSE-SURFACE METHOD ; ADAPTIVE EXPERIMENTAL-DESIGN ; SMALL FAILURE PROBABILITIES ; WEIGHTED REGRESSION ; SUBSET SIMULATION ; SURROGATE MODELS ; NEURAL-NETWORKS ; OPTIMIZATION ; CONSTRUCTION ; SYSTEM
研究领域[WOS]: Engineering ; Operations Research & Management Science
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/19421
Appears in Collections:空间自动化技术研究室_期刊论文

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Recommended Citation:
Sun ZL,Wang J,Li R,et al. LIF: A new Kriging based learning function and its application to structural reliability analysis[J]. RELIABILITY ENGINEERING & SYSTEM SAFETY,2017,157:152-165.
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