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

Chaoyang Xie,Guijie Li

doi:10.1155/2016/6419058

Abstract

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.

Highlights

The uncertainties of material properties, environment loads, and design models are inevitable in engineering
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 that are made under uncertainty [9]
The mixed uncertainty propagation approach is proposed by integrated adaptive sampling method and Kriging model for QMU analysis in this paper

Summary

Introduction

The uncertainties of material properties, environment loads, and design models are inevitable in engineering. In order to overcome the lack of probabilistic method, the nonprobabilistic methods have been proposed and are more suitable to handle the epistemic uncertainty based on the Fuzzy theory, interval theory, evidence theory, and so forth. The theoretical concept and the application of the Quantification of Margins and Uncertainties (QMU) methodology were reported in the certification of reliability, safety of nuclear weapons stockpile, and risk-informed decision making process under restriction of test data in the last decade. The objective of this paper is to propose an implementation framework of QMU under mixed uncertainty based on the evidence theory. To alleviate the computational costs, a stochastic surrogate model based on Kriging model and adaptive sampling method has been applied for uncertainty propagation for structure performance response.

The Concept and Metric of QMU

The Mixed Uncertainty Propagation Based on Evidence Theory

Application Example for Structure Analysis

Conclusion