Estimation of Failure Probability Function under Imprecise Probabilities by Active Learning–Augmented Probabilistic Integration

2021 ◽  
Vol 7 (4) ◽  
pp. 04021054
Author(s):  
Chao Dang ◽  
Pengfei Wei ◽  
Jingwen Song ◽  
Michael Beer
Keyword(s):  
Active Learning ◽  
Failure Probability ◽  
Probability Function ◽  
Imprecise Probabilities

A Modified Supervaluationist Framework for Decision-Making

2021 ◽  
Vol 12 (2) ◽  
pp. 175-191
Author(s):  
Jonas Karge ◽  
Keyword(s):  
Decision Making ◽  
Probability Function ◽  
Imprecise Probabilities ◽  
Adequate Account ◽  
Alternative Account ◽  
Semantic Approach ◽  
Probability Functions ◽  
Degree Of Belief ◽  
The Way

How strongly an agent beliefs in a proposition can be represented by her degree of belief in that proposition. According to the orthodox Bayesian picture, an agent's degree of belief is best represented by a single probability function. On an alternative account, an agent’s beliefs are modeled based on a set of probability functions, called imprecise probabilities. Recently, however, imprecise probabilities have come under attack. Adam Elga claims that there is no adequate account of the way they can be manifested in decision-making. In response to Elga, more elaborate accounts of the imprecise framework have been developed. One of them is based on supervaluationism, originally, a semantic approach to vague predicates. Still, Seamus Bradley shows that some of those accounts that solve Elga’s problem, have a more severe defect: they undermine a central motivation for introducing imprecise probabilities in the first place. In this paper, I modify the supervaluationist approach in such a way that it accounts for both Elga’s and Bradley’s challenges to the imprecise framework.

scholarly journals Resonance risk and global sensitivity analysis of a straight–curved combination pipe based on active learning Kriging model

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401983835
Author(s):  
Yuzhen Zhao ◽  
Yongshou Liu ◽  
Qing Guo ◽  
Tao Han ◽  
Baohui Li
Keyword(s):  
Sensitivity Analysis ◽  
Active Learning ◽  
Coordinate System ◽  
Failure Probability ◽  
Global Sensitivity Analysis ◽  
Dynamic Stiffness ◽  
Kriging Model ◽  
Global Sensitivity ◽  
Pipes Conveying Fluid ◽  
Conveying Fluid

The resonance failure of straight–curved combination pipes conveying fluid which are widely used in engineering is becoming a serious issue. But there are only few studies available on the resonance failure of combination pipes. The resonance failure probability and global sensitivity analysis of straight–curved combination pipes conveying fluid are studied by the active learning Kriging method proposed in this article. Based on the Euler–Bernoulli beam theory, the dynamic stiffness matrices of straight and curved pipes are derived in the local coordinate system, respectively. Then the dynamic stiffness matrix and characteristic equation of a straight–curved combination pipe conveying fluid are assembled under a global coordinate system. The natural frequency is calculated based on the characteristic equation. A resonance failure performance function is established based on the resonance failure mechanism and relative criterions. The active learning Kriging model based on expected risk function is introduced for calculating the resonance failure probability and moment-independent global sensitivity analysis index. The importance rankings of input variables are obtained with different velocities. According to the results, it is shown that the method proposed in this article provides a lot of guidance for resonance reliability analysis and anti-resonance design in combination pipes conveying fluid.

Resonance failure sensitivity analysis of straight-tapered assembled pipes conveying nonuniform axial fluid by active learning Kriging method

2019 ◽  
Vol 15 (5) ◽  
pp. 975-989
Author(s):  
Yuzhen Zhao ◽  
Wei Liu ◽  
Qing Guo ◽  
Zijun Zhang
Keyword(s):  
Sensitivity Analysis ◽  
Active Learning ◽  
Natural Frequency ◽  
Critical Velocity ◽  
Failure Probability ◽  
Motion Equation ◽  
Content Type ◽  
Nonuniform Fluid ◽  
Importance Ranking ◽  
Uniform Fluid

Purpose The purpose of this paper is to study the resonance failure sensitivity analysis of straight-tapered assembled pipe conveying nonuniform axial fluid by an active learning Kriging (ALK) method. Design/methodology/approach In this study, first, the motion equation of straight-tapered assembled pipe conveying nonuniform fluid is built. Second, the Galerkin method is used for calculating the natural frequency of assembled pipe conveying nonuniform fluid. Third, the ALK method based on expected risk function (ERF) is used to calculate the resonance failure probability and moment independent global sensitivity analysis. Findings The findings of this paper highlight that the eigenfrequency and critical velocity of uniform fluid-conveying pipe are less than the reality and the error is biggest in first-order natural frequency. The importance ranking of input variables affecting the resonance failure can be obtained. The importance ranking is different for a different velocity and mode number. By reducing the uncertainty of variables with a high index, the resonance failure probability can be reduced maximally. Research limitations/implications There are no experiments on the eigenfrequency and critical velocity. There is no experiments about natural frequency and critical velocity of straight tapered assembled pipe to verify the theory in this paper. Originality/value The originality of this paper lies as follows: the motion equation of straight-tapered pipe conveying nonuniform fluid is first obtained. The eigenfrequency of nonuniform fluid and uniform fluid inside the assembled pipe are compared. The resonance reliability analysis of straight-tapered assembled pipe is first proposed. From the results, it is observed that the resonance failure probability can be reduced efficiently.

Efficient procedure for failure probability function estimation in augmented space

2021 ◽  
Vol 92 ◽  
pp. 102104
Author(s):  
Xiukai Yuan ◽  
Shaolong Liu ◽  
M.A. Valdebenito ◽  
Jian Gu ◽  
Michael Beer
Keyword(s):  
Failure Probability ◽  
Probability Function ◽  
Function Estimation ◽  
Efficient Procedure ◽  
Augmented Space
Sign in / Sign up

Export Citation Format

Share Document