An integrated QMU approach to structural reliability assessment based on evidence theory and kriging model with adaptive sampling

Xie, Cheng; Li, Guijie; Wei, Fayuan

doi:10.1016/j.ress.2017.11.014

Cited by 15 publications

(5 citation statements)

References 43 publications

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“…As a result, the trends of carbon emissions are affected substantially by the specification of these two input variables. The equipment peak value has two discontinues intervals [13,15] and [16,17], which leads to significant jumps of CBF and CPF in Figure 7a and Figure 7b. The overlapping intervals from lighting peak values ( [6,8] and [7,9]) also have important influence on carbon emissions.…”

Section: Annual Heating Energymentioning

confidence: 99%

“…The Dempster-Shafer theory (DST) of evidence can be regarded as a generalization of classical probability theory that allows one to deal with the imprecise information on data, often in the form of interval-valued data. The mathematical foundations of DST analysis have been well established [14] and the DST approach has been used in various fields, including studies on reliability of pressure vessels [15], petroleum engineering [16], urban environment [17], and computer voice detection [18]. More recently, the DST analysis is also being applied to the analysis of building energy.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty and sensitivity analysis of energy assessment for office buildings based on Dempster-Shafer theory

Tian

Wilde

et al. 2018

Energy Conversion and Management

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Uncertainty and sensitivity analysis of building energy has become an active research area in order to consider variations of input variables and identify key variables influencing building energy. When there is only limited information available for uncertainty of building inputs, a specific probability for a given variable cannot be defined. Then, it is necessary to develop alternative approaches to probabilistic uncertainty and sensitivity analysis for building energy. Therefore, this paper explores the application of the Dempster-Shafer theory (DST) of evidence to conduct uncertainty and sensitivity analysis for buildings. The DST method is one of imprecise probability theories to allow combining uncertainty from different sources in terms of interval-valued probabilities in order to construct the belief and plausibility (two uncertainty measures) of system responses. The results indicate that the DST uncertainty analysis in combination with machine learning methods can provide fast and reliable information on uncertainty of building energy. It is recommended that at least two inherently different learning algorithms should be applied to provide robust simulation results of building energy. A spectrum of distributions should be implemented in global sensitivity analysis with the DST method because there are no specific distributions for intervals of input factors. Moreover, the stability of results from uncertainty and sensitivity analysis should be assessed when applying the DST method in building energy analysis.

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Section: Annual Heating Energymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Uncertainty and sensitivity analysis of energy assessment for office buildings based on Dempster-Shafer theory

Tian

Wilde

et al. 2018

Energy Conversion and Management

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“…The parametric uncertainty is propagated by calculating the plausibility and belief measures for the risk index, which comprise an interval that bounds the probabilistic risk index. 37 For example, Xie et al 38 used evidence theory to describe the parametric uncertainty in a PRA model of a pressure vessel subject to corrosion and developed a kriging model-based adaptive sampling method for effective risk assessment.…”

Section: Literature Reviewmentioning

confidence: 99%

An integrated risk index accounting for epistemic uncertainty in probability risk assessment

Zeng

Bani-Mustafa

Flage

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part O:

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In this paper, we present an integrated framework for quantifying epistemic uncertainty in probabilistic risk assessment. Three types of epistemic uncertainty, that is, completeness, structural and parametric uncertainties, are considered. A maturity model is developed to evaluate the management of these epistemic uncertainties in the model building process. The impact of epistemic uncertainty on the result of the risk assessment is, then, estimated based on the developed maturity model. Then, an integrated risk index is defined to reflect the epistemic uncertainty in the risk assessment results. An indifference method is developed to evaluate the index based on the maturity of epistemic uncertainty management. A case study concerning a nuclear power plant is shown to demonstrate the applicability of the overall modelling framework.

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“…Harsheel et al [30] implemented Dempster-Shafer Theory of Evidence in the presence of mixed uncertainties in the reliability and performance assessment of complex engineering systems through the use of the QMU methodology. Xie et al [39] proposed to use the QMU for simulation-based structural analysis of a pressure vessel with corrosion damage. But there is still little available work about using the QMU methodology in the stability analysis of aeroelastic system considering both the aleatory and epistemic uncertainties.…”

Section: Introductionmentioning

confidence: 99%

A Two-Phase Monte Carlo Simulation/Non-Intrusive Polynomial Chaos (MSC/NIPC) Method for Quantification of Margins and Mixed Uncertainties (QMMU) in Flutter Analysis

Zhang

Yang

2020

IEEE Access

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A two-phase Monte Carlo Simulation/Non-intrusive Polynomial Chaos (MCS/NIPC) method for quantification of margins and mixed uncertainties (aleatory and epistemic uncertainties) is proposed in this paper for the flutter speed boundary analysis. Compared with the traditional MCS/MCS method which needs lots of numerical simulations, the MCS/NIPC method can reduce the computational cost without losing accuracy due to the use of point collocation non-intrusive polynomial chaos in the inner loop. Based on the results of uncertainty quantification, a novel practical quantification of margins and mixed uncertainties (QMMU) framework is proposed considering both aleatory and epistemic uncertainties such as the parametric uncertainties in complicated models. A two-dimensional airfoil and a three-dimensional benchmark wing, the AGARD 445.6 wing, are both employed to illustrate the practical application of the proposed methods for flutter speed computation in the presence of mixed uncertainties arising from the material properties and flight conditions. Within the analysis, aleatory and mixed uncertainty quantification are conducted to prove the effectiveness and efficiency of the MCS/NIPC method. Then the QMMU metrics are accomplished for the flutter speed boundary analysis of the two airfoil models. The results demonstrate the potential of the proposed QMMU framework to analyze the stability of engineering systems. INDEX TERMS Flutter, non-intrusive polynomial chaos, probability bounds, quantification of margins and mixed uncertainties, uncertainty quantification

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An integrated QMU approach to structural reliability assessment based on evidence theory and kriging model with adaptive sampling

Cited by 15 publications

References 43 publications

Uncertainty and sensitivity analysis of energy assessment for office buildings based on Dempster-Shafer theory

Uncertainty and sensitivity analysis of energy assessment for office buildings based on Dempster-Shafer theory

An integrated risk index accounting for epistemic uncertainty in probability risk assessment

A Two-Phase Monte Carlo Simulation/Non-Intrusive Polynomial Chaos (MSC/NIPC) Method for Quantification of Margins and Mixed Uncertainties (QMMU) in Flutter Analysis

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