.png){kind=icon}
返回
Application of line sampling simulation method to reliability benchmark problems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.strusafe.2006.07.009.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A procedure denoted as Line Sampling (LS) has been developed for estimating the reliability of static and dynamical systems. The efficiency and accuracy of the method is shown by application to the subset of the entire spectrum of the posed benchmark problems [Schueller GI, Pradlwarter HJ, Koutsourelakis PS. Benchmark study on reliability estimation in higher dimensions of structural systems. In URL: http://www.uibk.ac.at/mechanik/Publications/benchmark.html. Institute of Engineering Mechanics, Leopold-Franzens University, Innsbruck, Austria, 2004], i.e. in particular linear systems with random properties. The notion of design point excitation for non-linear systems is discussed and its use extended for reliability estimations of conservative non-linear MDOF systems considering critical conditional excitation. For solving the hysteretic MDOF system with uncertain structural parameters subjected to general Gaussian excitation, however, the general applicable subset procedure [An S K, Beck J L. Estimation of small failure probabilities in high dimensions by subset simulation. Probab Eng Mech 2001;16:263-277] has been used combined with Importance Sampling. (c) 2006 Elsevier Ltd. All rights reserved.
Keyword:
structural reliability
Monte Carlo simulations
line sampling
structural uncertainty
non-linear systems
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.3
论文数:
1.4K
被引数:
7.0K
机构
暂无机构信息
引用论文
Benchmark study on reliability estimation in higher dimensions of structural systems -: An overview.png)
STRUCTURAL SAFETY
STRUCTURAL SAFETYIF6.3
PREAI
分享收藏Estimation of small failure probabilities in high dimensions by subset simulation通过子集模拟估计高维中的小失效概率
OAAI
OAAI分享收藏Reliability of structures in high dimensions, part I:: algorithms and applications高维结构的可靠性，第一部分: 算法和应用
PREAI
分享收藏分享收藏 没有更多内容
没有更多内容