Quantification of Margins and Uncertainties Approach for Structure Analysis Based on Evidence Theory

Joint Authors

Guijie, Li
Xie, Chaoyang

Source

Mathematical Problems in Engineering

Issue

Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-5, 5 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2016-06-19

Country of Publication

Egypt

No. of Pages

5

Main Subjects

Civil Engineering

Abstract EN

Quantification of Margins and Uncertainties (QMU) is a decision-support methodology for complex technical decisions centering on performance thresholds and associated margins for engineering systems.

Uncertainty propagation is a key element in QMU process for structure reliability analysis at the presence of both aleatory uncertainty and epistemic uncertainty.

In order to reduce the computational cost of Monte Carlo method, a mixed uncertainty propagation approach is proposed by integrated Kriging surrogate model under the framework of evidence theory for QMU analysis in this paper.

The approach is demonstrated by a numerical example to show the effectiveness of the mixed uncertainty propagation method.

American Psychological Association (APA)

Xie, Chaoyang& Guijie, Li. 2016. Quantification of Margins and Uncertainties Approach for Structure Analysis Based on Evidence Theory. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-5.
https://search.emarefa.net/detail/BIM-1112453

Modern Language Association (MLA)

Xie, Chaoyang& Guijie, Li. Quantification of Margins and Uncertainties Approach for Structure Analysis Based on Evidence Theory. Mathematical Problems in Engineering No. 2016 (2016), pp.1-5.
https://search.emarefa.net/detail/BIM-1112453

American Medical Association (AMA)

Xie, Chaoyang& Guijie, Li. Quantification of Margins and Uncertainties Approach for Structure Analysis Based on Evidence Theory. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-5.
https://search.emarefa.net/detail/BIM-1112453

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1112453