Paper Info
Title
Global Reliability Sensitivity Analysis Based on Maximum Entropy and 2-Layer Polynomial Chaos Expansion.
Abstract
To optimize contributions of uncertain input variables on the statistical parameter of given model, e.g., reliability, global reliability sensitivity analysis (GRSA) provides an appropriate tool to quantify the effects. However, it may be difficult to calculate global reliability sensitivity indices compared with the traditional global sensitivity indices of model output, because statistical parameters are more difficult to obtain, Monte Carlo simulation (MCS)-related methods seem to be the only ways for GRSA but they are usually computationally demanding. This paper presents a new non-MCS calculation to evaluate global reliability sensitivity indices. This method proposes: (i) a 2-layer polynomial chaos expansion (PCE) framework to solve the global reliability sensitivity indices; and (ii) an efficient method to build a surrogate model of the statistical parameter using the maximum entropy (ME) method with the moments provided by PCE. This method has a dramatically reduced computational cost compared with traditional approaches. Two examples are introduced to demonstrate the efficiency and accuracy of the proposed method. It also suggests that the important ranking of model output and associated failure probability may be different, which could help improve the understanding of the given model in further optimization design.
Year
DOI
Venue
2018
10.3390/e20030202
ENTROPY
Keywords
Field
DocType
global reliability sensitivity analysis,polynomial chaos expansion,Sobol's indices,the maximum entropy method
Statistical parameter,Monte Carlo method,Mathematical optimization,Ranking,Surrogate model,Polynomial chaos,Principle of maximum entropy,Mathematics
Journal
Volume
Issue
ISSN
20
3
1099-4300
Citations 
PageRank 
References 
0
0.34
12
Authors
4
Name
Order
Citations
PageRank
Jianyu Zhao112.42
Shengkui Zeng2194.71
Jianbin Guo332.43
Shaohua Du400.34