AK-SYS: An adaptation of the AK-MCS method for system reliability

A lot of research work has been proposed over the last two decades to evaluate the probability of failure of a structure involving a very time-consuming mechanical model. Surrogate model approaches based on Kriging, such as the Efficient Global Reliability Analysis (EGRA) or the Active learning and...

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Bibliographic Details
Published in:Reliability engineering & system safety Vol. 123; pp. 137 - 144
Main Authors: Fauriat, W., Gayton, N.
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.03.2014
Elsevier
Subjects:
Adaptation
AK-MCS
Applied sciences
Classification
Computer simulation
Engineering Sciences
Exact sciences and technology
Failure
Fracture mechanics (crack, fatigue, damage...)
Fundamental areas of phenomenology (including applications)
Kriging
Learning
Linear inference, regression
Mathematical analysis
Mathematics
Monte Carlo methods
Monte-Carlo simulation
Operational research and scientific management
Operational research. Management science
Parametric inference
Physics
Probability and statistics
Reliability theory. Replacement problems
Sampling
Sciences and techniques of general use
Solid mechanics
Statistics
Structural and continuum mechanics
System reliability
AK-MCS
System reliability
Sampling
Monte-Carlo simulation
Kriging
Classification
Monte Carlo method
Probabilistic approach
Program optimization
High precision
Rupture
Global analysis
Modeling
Learning systems
Least squares method
High efficiency
Metamodel
ISSN:0951-8320, 1879-0836
Online Access:Get full text
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Summary:A lot of research work has been proposed over the last two decades to evaluate the probability of failure of a structure involving a very time-consuming mechanical model. Surrogate model approaches based on Kriging, such as the Efficient Global Reliability Analysis (EGRA) or the Active learning and Kriging-based Monte-Carlo Simulation (AK-MCS) methods, are very efficient and each has advantages of its own. EGRA is well suited to evaluating small probabilities, as the surrogate can be used to classify any population. AK-MCS is built in relation to a given population and requires no optimization program for the active learning procedure to be performed. It is therefore easier to implement and more likely to spend computational effort on areas with a significant probability content. When assessing system reliability, analytical approaches and first-order approximation are widely used in the literature. However, in the present paper we rather focus on sampling techniques and, considering the recent adaptation of the EGRA method for systems, a strategy is presented to adapt the AK-MCS method for system reliability. The AK-SYS method, “Active learning and Kriging-based SYStem reliability method”, is presented. Its high efficiency and accuracy are illustrated via various examples.
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ISSN:0951-8320
1879-0836
DOI:10.1016/j.ress.2013.10.010