An efficient and robust structural reliability analysis method with mixed variables based on hybrid conjugate gradient direction

被引:15
作者
Huang, Peng [1 ,2 ]
Huang, Hong-Zhong [1 ,2 ]
Li, Yan-Feng [1 ,2 ]
Qian, Hua-Ming [1 ,2 ]
机构
[1] Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu 611731, Sichuan, Peoples R China
[2] Univ Elect Sci & Technol China, Ctr Syst Reliabil & Safety, Chengdu, Peoples R China
基金
国家重点研发计划;
关键词
hybrid reliability analysis; probabilistic analysis; interval analysis; first-order reliability method; projected gradient method; DESIGN OPTIMIZATION; CHAOS CONTROL; MODEL;
D O I
10.1002/nme.6609
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Traditional reliability analysis is based on probability theory with precise distributions. However, determining the distribution of all variables precisely is impossible due to insufficient information. Therefore, random and interval variables may be encountered, and probabilistic reliability methods are hard to use. The existing interval variables make reliability analysis more difficult. In this article, an efficient and robust hybrid reliability analysis method is proposed for structures with both random and interval variables. Firstly, a single-loop procedure is proposed by performing probabilistic analysis and interval analysis simultaneously in each most probable point search process. Then an improved conjugate sensitivity factor method based on hybrid conjugate gradient direction and adaptive finite step length is developed for probabilistic analysis. Meanwhile, the hybrid conjugate gradient direction together with active set is introduced into the projected gradient method for interval analysis. Finally, a comparison analysis with six numerical examples is provided to validate the performance of the proposed method. The results demonstrate that the proposed method is better than the existing methods in terms of efficiency and robustness for hybrid reliability analysis with random and interval variables.
引用
收藏
页码:1990 / 2004
页数:15
相关论文
共 48 条
[1]  
Ben-Haim Y., 1990, Convex Models of Uncertainty in Applied Mechanics
[2]   An Active Set Modified Polak-Ribi,re-Polyak Method for Large-Scale Nonlinear Bound Constrained Optimization [J].
Cheng, Wanyou ;
Li, Donghui .
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS, 2012, 155 (03) :1084-1094
[3]  
Du, 2017, STRUCT MULTIDISCIP O, V56, P1
[4]   An inverse analysis method for design optimization with both statistical and fuzzy uncertainties [J].
Du, Liu ;
Choi, K. K. .
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2008, 37 (02) :107-119
[5]   Saddlepoint approximation for sequential optimization and reliability analysis [J].
Du, Xiaoping .
JOURNAL OF MECHANICAL DESIGN, 2008, 130 (01)
[6]  
Fletcher R., 1987, Practical methods of optimization, V2nd, DOI DOI 10.1002/9781118723203
[7]   A robust iterative algorithm for structural reliability analysis [J].
Gong, Jin-Xin ;
Yi, Ping .
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2011, 43 (04) :519-527
[8]   Reliability Analysis for Multidisciplinary Systems with Random and Interval Variables [J].
Guo, Jia ;
Du, Xiaoping .
AIAA JOURNAL, 2010, 48 (01) :82-91
[9]   Reliability sensitivity analysis with random and interval variables [J].
Guo, Jia ;
Du, Xiaoping .
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, 2009, 78 (13) :1585-1617
[10]  
Han, 2019, ANN OPER RES, DOI [10.1007/s10479, DOI 10.1007/S10479]