Epistemic uncertainty quantification in metal fatigue crack growth analysis using evidence theory

被引：26

作者：

Tang, Hesheng ^{[1
]}

Li, Dawei ^{[2
]}

Li, Jingjing ^{[2
]}

Xue, Songtao ^{[2
]}

机构：

[1] Tongji Univ, State Key Lab Disaster Reduct Civil Engn, 1239 Siping Rd, Shanghai, Peoples R China

[2] Tongji Univ, Res Inst Struct Engn & Disaster Reduct, 1239 Siping Rd, Shanghai, Peoples R China

来源：

INTERNATIONAL JOURNAL OF FATIGUE | 2017年 / 99卷

基金：

中国国家自然科学基金; 上海市自然科学基金;

关键词：

Evidence theory; Fatigue crack growth; Epistemic uncertainty; Uncertainty quantification; Differential evolution; DESIGN; OPTIMIZATION; RELIABILITY; FAILURE; PROBABILITIES; PARAMETERS; PROGNOSIS; EQUATION; STEEL; STATE;

D O I：

10.1016/j.ijfatigue.2017.03.004

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

Uncertainties originate from physical variability, data uncertainty, and modeling errors in the fatigue crack growth prediction analysis. This study presents an evidential uncertainty quantification (UQ) approach for determining uncertainties involved in revealing the material constants of the metal fatigue crack growth model with imprecise uncertainty information (i.e., epistemic uncertainty). The parameters in fatigue crack growth model are obtained by fitting the available sparse experimental data, and then the uncertainties in these parameters are considered. To alleviate the computational difficulties in the UQ analysis based on evidence theory, an interval optimization method based on differential evolution is used in finding the propagated belief structure. The overall procedure is demonstrated using the results of several replicated experiments on aluminum alloy CCT specimens. (C) 2017 Elsevier Ltd. All rights reserved.

引用

页码：163 / 174

页数：12

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