A Structural Reliability Analysis Method Based on Radial Basis Function

IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Cmc-computers Materials & Continua Pub Date : 2012-02-01 DOI:10.3970/CMC.2012.027.128
M. Chau, Xu Han, Y. Bai, C. Jiang
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引用次数: 13

Abstract

The first-order reliability method (FORM) is one of the most widely used structural reliability analysis techniques due to its simplicity and efficiency. However, direct using FORM seems disability to work well for complex problems, especially related to high-dimensional variables and computation intensive numerical models. To expand the applicability of the FORM for more practical engineering problems, a response surface (RS) approach based FORM is proposed for structural reliability analysis. The radial basis function (RBF) is employed to approximate the implicit limit-state functions combined with Latin Hypercube Sampling (LHS) strategy. To guarantee the numerical stability, the improved HL-RF (iHLRF) algorithm is used to assess the reliability index and corresponding probability of failure based on the constructed RS model. The effectiveness of the proposed method is demonstrated through five numerical examples.
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基于径向基函数的结构可靠性分析方法
一阶可靠度法(FORM)由于其简单、高效的优点,是目前应用最广泛的结构可靠度分析方法之一。然而,直接使用FORM似乎不能很好地解决复杂的问题,特别是与高维变量和计算密集型数值模型相关的问题。为了扩大FORM在更多实际工程问题中的适用性,提出了一种基于响应面法的结构可靠性分析方法。采用径向基函数(RBF)逼近隐式极限状态函数,结合拉丁超立方采样(LHS)策略。为了保证数值稳定性,基于所构建的RS模型,采用改进的HL-RF (iHLRF)算法对可靠性指标和相应的失效概率进行评估。通过5个算例验证了该方法的有效性。
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来源期刊
Cmc-computers Materials & Continua
Cmc-computers Materials & Continua 工程技术-材料科学:综合
CiteScore
5.30
自引率
19.40%
发文量
345
审稿时长
1 months
期刊介绍: This journal publishes original research papers in the areas of computer networks, artificial intelligence, big data management, software engineering, multimedia, cyber security, internet of things, materials genome, integrated materials science, data analysis, modeling, and engineering of designing and manufacturing of modern functional and multifunctional materials. Novel high performance computing methods, big data analysis, and artificial intelligence that advance material technologies are especially welcome.
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