Structural reliability analysis algorithm in the presence of random and interval variables

被引：0
|
作者：
Qiu T. [1 ,2 ,3 ]
Wang Z. [1 ]
You L. [2 ,3 ]
机构：
[1] The 28th Research Institute, China Electronics Technology Group Corporation, Nanjing
[2] School of Realibility and System Engineering, Beihang University, Beijing
[3] National Defense Key Laboratory of Science and Technology on Reliability and Environmental Engineering, Beihang University, Beijing
来源：
| 1600年 / CIMS卷 / 26期
基金：
中国国家自然科学基金;
关键词：
Adjustment parameters; Design point; Interval variables; Quadratic interpolation sampling; Structural reliability;
D O I：
10.13196/j.cims.2020.11.009
中图分类号：
学科分类号：
摘要：
Aiming at the structural reliability problem in the presence of random and interval variables, an efficient sequence iterative reliability analysis algorithm was proposed. The High Dimensional Model Representation method (HDMR) was used to decouple the reliability model of probability-interval mixture into a single-layer reliability model with random and interval variable separation. Based on the Quadratic Interpolation Sampling method (QIS), unit functions that represented random and interval variables were fitted. In probability analysis, two adjustment parameters were introduced to control the search direction and search step size of optimizing design point respectively. In interval analysis, the projection gradient method was used to calculate interval optimal point, and the failure probability was solved by sequence iteration algorithm. Compared with the Monte Carlo Sampling method (MCS), the relative error of the algorithm was less than 5%, which verified the accuracy and efficiency. In addition, when the degree of nonlinearity of limit state function was high, the algorithm could also ensure the convergence of the calculation results. © 2020, Editorial Department of CIMS. All right reserved.
引用
收藏
页码：2992 / 3000
页数：8
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