A Gaussian process-based dynamic surrogate model for complex engineering structural reliability analysis

2017 ◽  
Vol 68 ◽  
pp. 97-109 ◽  
Author(s):  
Guoshao Su ◽  
Lifeng Peng ◽  
Lihua Hu
Keyword(s):  
Gaussian Process ◽  
Reliability Analysis ◽  
Surrogate Model ◽  
Structural Reliability ◽  
Structural Reliability Analysis ◽  
Complex Engineering

scholarly journals Structural Reliability Analysis via the Multivariate Gegenbauer Polynomial-Based Sparse Surrogate Model

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Yixuan Dong ◽  
Shijie Wang
Keyword(s):  
Reliability Analysis ◽  
Surrogate Model ◽  
Structural Reliability ◽  
Matching Pursuit ◽  
Computational Cost ◽  
Performance Function ◽  
Explanatory Variables ◽  
Structural Reliability Analysis ◽  
Gegenbauer Polynomial ◽  
Potential Applications

Structural reliability analysis is usually realized based on a multivariate performance function that depicts failure mechanisms of a structural system. The intensively computational cost of the brutal-force Monte-Carlo simulation motivates proposing a Gegenbauer polynomial-based surrogate model for effective structural reliability analysis in this paper. By utilizing the orthogonal matching pursuit algorithm to detect significant explanatory variables at first, a small number of samples are used to determine a reliable approximation result of the structural performance function. Several numerical examples in the literature are presented to demonstrate potential applications of the Gegenbauer polynomial-based sparse surrogate model. Accurate results have justified the effectiveness of the proposed approach in dealing with various structural reliability problems.

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