An efficient epistemic uncertainty analysis method for structural-acoustic problem based on evidence theory

2018 ◽  
Vol 66 (2) ◽  
pp. 117-130
Author(s):  
Ning Chen ◽  
Shengwen Yin ◽  
Dejie Yu ◽  
Zuojun Liu ◽  
Baizhan Xia
Keyword(s):  
Uncertainty Analysis ◽  
Epistemic Uncertainty ◽  
Evidence Theory ◽  
Analysis Method ◽  
Acoustic Problem

Evidence-theory-based kinematic uncertainty analysis of a dual crane system with epistemic uncertainty

2021 ◽  
pp. 1-29
Author(s):  
Bin Zhou ◽  
Bin Zi ◽  
Yishang Zeng ◽  
Weidong Zhu
Keyword(s):  
Uncertainty Analysis ◽  
Perturbation Method ◽  
Epistemic Uncertainty ◽  
Evidence Theory ◽  
Taylor Series Expansion ◽  
Computational Time ◽  
Evidence Based ◽  
Interval Equations ◽  
Basic Probability ◽  
Interval Perturbation Method

Abstract An evidence-theory-based interval perturbation method (ETIPM) and an evidence-theory-based subinterval perturbation method (ETSPM) are presented for the kinematic uncertainty analysis of a dual cranes system (DCS) with epistemic uncertainty. A multiple evidence variable (MEV) model that consists of evidence variables with focal elements (FEs) and basic probability assignments (BPAs) is constructed. Based on the evidence theory, an evidence-based kinematic equilibrium equation with the MEV model is equivalently transformed to several interval equations. In the ETIPM, the bounds of the luffing angular vector (LAV) with respect to every joint FE are calculated by integrating the first-order Taylor series expansion and interval algorithm. The bounds of the expectation and variance of the LAV and corresponding BPAs are calculated by using the evidence-based uncertainty quantification method. In the ETSPM, the subinterval perturbation method is introduced to decompose original FE into several small subintervals. By comparing results yielded by the ETIPM and ETSPM with those by the evidence theory-based Monte Carlo method, numerical examples show that the accuracy and computational time of the ETSPM are higher than those of the ETIPM, and the accuracy of the ETIPM and ETSPM can be significantly improved with the increase of the number of FEs and subintervals.

Uncertainty Analysis With Probability and Evidence Theories

2006 ◽  
Author(s):  
Xiaoping Du
Keyword(s):  
Probability Theory ◽  
Uncertainty Analysis ◽  
Physical System ◽  
Epistemic Uncertainty ◽  
Evidence Theory ◽  
Future Research ◽  
Aleatory Uncertainty ◽  
Maximum Probability ◽  
Reliability Method ◽  
Input Parameters

Both aleatory and epistemic uncertainties exist in engineering applications. Aleatory uncertainty (objective or stochastic uncertainty) describes the inherent variation associated with a physical system or environment. Epistemic uncertainty, on the other hand, is derived from some level of ignorance or incomplete information about a physical system or environment. Aleatory uncertainty associated with parameters is usually modeled by probability theory and has been widely researched and applied by industry, academia, and government. The study of epistemic uncertainty in engineering has recently started. The feasibility of the unified uncertainty analysis that deals with both types of uncertainties is investigated in this paper. The input parameters with aleatory uncertainty are modeled with probability distributions by probability theory, and the input parameters with epistemic uncertainty are modeled with basic probability assignment by evidence theory. The effect of the mixture of both aleatory and epistemic uncertainties on the model output is modeled with belief and plausibility measures (or the lower and upper probability bounds). It is shown that the calculation of belief measure or plausibility measure can be converted to the calculation of the minimum or maximum probability of failure over each of the mutually exclusive subsets of the input parameters with epistemic uncertainty. A First Order Reliability Method (FORM) based algorithm is proposed to conduct the unified uncertainty analysis. Two examples are given for the demonstration. Future research directions are derived from the discussions in this paper.

Uncertainty Analysis in Fatigue Life Prediction of Concrete Using Evidence Theory

2016 ◽  
Vol 866 ◽  
pp. 25-30
Author(s):  
He Sheng Tang ◽  
Jia He Mei ◽  
Wei Chen ◽  
Da Wei Li ◽  
Song Tao Xue
Keyword(s):  
Experimental Data ◽  
Fatigue Life ◽  
Uncertainty Analysis ◽  
Model Uncertainty ◽  
Life Prediction ◽  
Epistemic Uncertainty ◽  
Uncertainty Propagation ◽  
Evidence Theory ◽  
Fatigue Life Prediction ◽  
Theory Method

Various sources of uncertainty exist in concrete fatigue life prediction, such as variability in loading conditions, material parameters, experimental data and model uncertainty. In this article, the uncertainty model of concrete fatigue life prediction based on the S-N curve is built, and the evidence theory method is presented for uncertainty analysis in fatigue life prediction of concrete while considering the epistemic uncertainty of the parameter of the model. Based on the experimental of concrete four-point bending beams, the evidence theory method is applied to quantify the epistemic uncertainty stem from experimental data and model uncertainty. To improve the efficiency of computation, a method of differential evolution is adopted to speedup the works of uncertainty propagation. The efficiency and feasibility of the proposed approach are verified through a comparative analysis of probability theory.

Uncertainty Analysis Using Fuzzy Random Set Theory

2016 ◽  
pp. 384-418
Author(s):  
Debabrata Datta
Keyword(s):  
Uncertainty Analysis ◽  
Set Theory ◽  
Practical Problem ◽  
Epistemic Uncertainty ◽  
Uncertainty Modeling ◽  
Random Set ◽  
Uncertain Variables ◽  
Definition Of ◽  
Hydrological Applications ◽  
Fuzzy Random

Uncertainty analysis of any physical model is always an essential task from the point of decision making analysis. Two kinds of uncertainties exist: (1) aleatory uncertainty which is due to randomness of the parameters of models of interest and (2) the epistemic uncertainty which is due to fuzziness of the parameters of the same models. So far both these uncertainties are addressed independently; however since in any practical problem both the types of uncertain variables present, it is required to address them jointly. In order to solve practical problems on uncertainty modeling, it is required to replace the abstract definition of hybrid set by fuzzy random set. Since uncertainty modeling using fuzzy random set has not been carried out so far, the present chapter will address the utility of fuzzy random set for uncertainty modeling on geotechnical and hydrological applications. This chapter will present the fundamentals of fuzzy random set and their application in uncertainty analysis.

Sign in / Sign up

Export Citation Format

Share Document