A Probabilistic and Interval Hybrid Reliability Analysis Method for Structures with Correlated Uncertain Parameters

2015 ◽  
Vol 12 (04) ◽  
pp. 1540006 ◽  
Author(s):  
C. Jiang ◽  
J. Zheng ◽  
B. Y. Ni ◽  
X. Han
Keyword(s):  
Reliability Analysis ◽  
Structural Reliability ◽  
Correlation Coefficients ◽  
Analysis Method ◽  
Analysis Model ◽  
Probability Interval ◽  
Uncertain Variables ◽  
Uncertainty Model ◽  
Sample Correlation ◽  
Hybrid Reliability

This paper proposes a probability-interval mixed uncertainty model considering parametric correlations and a corresponding structural reliability analysis method. First of all, we introduce the sample correlation coefficients to express the correlations between different kinds of uncertain variables including probability and interval variables. Then dependent parameters are transformed into independent ones through a matrix transformation. A reliability analysis model is put forward, and an efficient method is built to obtain the reliability index or failure probability interval of the structure. Finally, four numerical examples are provided to verify the validity of the method.

A Vine-Copula-Based Reliability Analysis Method for Structures With Multidimensional Correlation

2015 ◽  
Vol 137 (6) ◽  
Author(s):  
C. Jiang ◽  
W. Zhang ◽  
X. Han ◽  
B. Y. Ni ◽  
L. J. Song
Keyword(s):  
Reliability Analysis ◽  
Structural Reliability ◽  
Computational Method ◽  
Joint Probability Distribution ◽  
Analysis Method ◽  
Analysis Model ◽  
Copula Functions ◽  
Solution Algorithms ◽  
Reliability Method ◽  
Vine Copula

This paper proposed a vine-copula-based structural reliability analysis method which is an effective approach for performing a reliability analysis on complex multidimensional correlation problems. A joint probability distribution function (PDF) among multidimensional random variables was established using a vine copula function, based on which a reliability analysis model was constructed. Two solution algorithms were proposed to solve this reliability analysis model: one was based on Monte Carlo simulation (MCS) and another one was based on the first-order reliability method (FORM). The former method provides a generalized computational method for a reliability analysis based on vine copula functions and can provide so-called “precise solutions”; the latter method has high computational efficiency and can be used to solve actual complex engineering problems. Finally, three numerical examples were provided to verify the effectiveness of the method.

Wear Reliability Analysis Method Based on Random Process

2012 ◽  
Vol 155-156 ◽  
pp. 348-351
Author(s):  
Yu Tao Yan ◽  
Zhi Li Sun ◽  
Guang Wei Hu ◽  
Li Fang Liu
Keyword(s):  
Random Process ◽  
Reliability Analysis ◽  
Test Data ◽  
Design Method ◽  
Uniform Design ◽  
Surface Hardness ◽  
Analysis Method ◽  
Analysis Model ◽  
Function Fitting ◽  
Uniform Design Method

The random reliability analysis model for wear was established based on random process theory and wear research. The wear experiment scheme on load, sliding speed, surface hardness and bearing capacity of lubrication was instituted by the uniform design method, and carried out on a vertical universal friction/wear tester. The static wear predication model was established by the partial least squares based on test data. It is found that the wear extent obey to normal distribution by checkout of the probability paper and mathematic expectation function fitting and residuals distribution, the variance function fitting and residuals distribution based on the test data of random process. The reliability of definite working life was gained by calculating with established reliability model, and compared with test data, the analysis method is a valid method for wear reliability.

RCA-PCK: A new structural reliability analysis method based on PC-Kriging and radial centralized adaptive sampling strategy

2020 ◽  
pp. 095440622095771
Author(s):  
Zhenliang Yu ◽  
Zhili Sun ◽  
Runan Cao ◽  
Jian Wang ◽  
Yutao Yan
Keyword(s):  
Reliability Analysis ◽  
Failure Probability ◽  
Adaptive Sampling ◽  
Structural Reliability ◽  
Sampling Strategy ◽  
Kriging Model ◽  
Strong Nonlinearity ◽  
Analysis Method ◽  
Structural Reliability Analysis ◽  
Training Samples

To improve the efficiency and accuracy of reliability assessment for structures with small failure probability and time-consuming simulation, a new structural reliability analysis method (RCA-PCK) is proposed, which combines PC-Kriging model and radial centralized adaptive sampling strategy. Firstly, the PC-Kriging model is constructed by improving the basis function of Kriging model with sparse polynomials. Then, the sampling region which contributes a great impact on the failure probability is constructed by combining the radial concentration and important sampling technology. Subsequently, the k-means++ clustering technology and learning function LIF are adopted to select new training samples from each subdomains in each iteration. To avoid the sampling distance in one subdomain or the distance between the new training samples in two subdomains being too small, we construct a screening mechanism to ensure that the selected new training samples are evenly distributed in the limit state. In addition, a new convergence criterion is derived based on the relative error estimation of failure probability. Four benchmark examples are given to illustrate the convergence process, accuracy and stability of the proposed method. Finally, the transmission error reliability analysis of thermal-elastic coupled gears is carried out to prove the applicability of the proposed method RCA-PCK to the structures with strong nonlinearity and time-consuming simulation.

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