Structural reliability analysis under evidence theory using the active learning kriging model

被引：30
|
作者：
Yang, Xufeng [1 ]
Liu, Yongshou [2 ]
Ma, Panke [3 ]
机构：
[1] Southwest Jiaotong Univ, Sch Mech Engn, Dept Railway Vehicle Engn, Chengdu, Sichuan, Peoples R China
[2] Northwestern Polytech Univ, Dept Engn Mech, Xian, Peoples R China
[3] Sichuan Inst Bldg Res, Chengdu, Sichuan, Peoples R China
来源：
ENGINEERING OPTIMIZATION | 2017年 / 49卷 / 11期
关键词：
Reliability analysis; evidence theory; active learning; kriging model; MONTE-CARLO-SIMULATION; UNCERTAINTY QUANTIFICATION; SENSITIVITY-ANALYSIS; EPISTEMIC UNCERTAINTY; GLOBAL OPTIMIZATION; PROBABILITY; APPROXIMATION; DESIGN;
D O I：
10.1080/0305215X.2016.1277063
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Structural reliability analysis under evidence theory is investigated. It is rigorously proved that a surrogate model providing only correct sign prediction of the performance function can meet the accuracy requirement of evidence-theory-based reliability analysis. Accordingly, a method based on the active learning kriging model which only correctly predicts the sign of the performance function is proposed. Interval Monte Carlo simulation and a modified optimization method based on Karush-Kuhn-Tucker conditions are introduced to make the method more efficient in estimating the bounds of failure probability based on the kriging model. Four examples are investigated to demonstrate the efficiency and accuracy of the proposed method.
引用
收藏
页码：1922 / 1938
页数：17
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