A fast-convergence algorithm for reliability analysis based on the AK-MCS

•The algorithm performance of existing AK-MCS structures is sensitive to the Kriging trend and initial candidate size.•A new DoE selection method is developed based on clustering theory.•Multiple trends method is integrated into the new AK-MCS structure.•The algorithm efficiency of the proposed stru...

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Bibliographic Details
Published in:Reliability engineering & system safety Vol. 213; p. 107693
Main Authors: Xiong, Yifang, Sampath, Suresh
Format: Journal Article
Language:English
Published: Barking Elsevier Ltd 01.09.2021
Elsevier BV
Subjects:
Adaptive refinement
Algorithms
Clustering
Computer applications
Computer simulation
Computing time
Control methods
Kriging
Metamodels
Methods
Monte Carlo simulation
Reliability
Reliability analysis
Reliability engineering
Adaptive refinement
Reliability
Kriging
Monte Carlo simulation
ISSN:0951-8320, 1879-0836
Online Access:Get full text
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Summary:•The algorithm performance of existing AK-MCS structures is sensitive to the Kriging trend and initial candidate size.•A new DoE selection method is developed based on clustering theory.•Multiple trends method is integrated into the new AK-MCS structure.•The algorithm efficiency of the proposed structure is validated against existing methods using four engineering examples. In the field of reliability engineering, assessing the probability of failure of an event is usually a computationally demanding task. One way of tackling this issue is by metamodelling, in which the original computational-expensive model is approximated by a simpler metamodel. A method called AK-MCS for Active learning reliability method combining Kriging and Monte Carlo Simulation, was developed for the metamodel construction, and proved effective in reliability analysis. However, the performance of the AK-MCS algorithm is sensitive to the candidate size and the Kriging trend. Moreover, it cannot take advantage of parallel computing, a highly efficient way to speed up the simulation process. Focusing on the identified issues, this study proposes three methods to improve algorithm performance: the candidate size control method, multiple trends method, and weighted clustering method. These three methods are integrated into the AK-MCS structure, with their individual and combined performances being tested using four examples. Results suggest that all three methods contribute to the improvement of algorithm efficiency. When the three methods working together, the computational time is reduced significantly, and in the meantime, higher accuracy can be achieved.
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ISSN:0951-8320
1879-0836
DOI:10.1016/j.ress.2021.107693