An improved adaptive kriging-based importance technique for sampling multiple failure regions of low probability

The estimation of system failure probabilities may be a difficult task when the values involved are very small, so that sampling-based Monte Carlo methods may become computationally impractical, especially if the computer codes used to model the system response require large computational efforts, b...

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Bibliographic Details
Published in:Reliability engineering & system safety Vol. 131; pp. 109 - 117
Main Authors: Cadini, F., Santos, F., Zio, E.
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.11.2014
Elsevier
Subjects:
Adaptive kriging
Algorithms
Applied sciences
Computation
Engineering Sciences
Exact sciences and technology
Failure
Flexibility
Importance sampling
Linear inference, regression
Mathematics
Multiple failure regions
Operational research and scientific management
Operational research. Management science
Probability and statistics
Reliability theory. Replacement problems
Sampling
Sampling theory, sample surveys
Sciences and techniques of general use
Small failure probabilities
Statistics
Strategy
Tasks
Adaptive kriging
Multiple failure regions
Small failure probabilities
Importance sampling
Monte Carlo method
Failures
Flexibility
Modeling
Adaptive method
Kriging
Variance reduction
Alternative method
Multiple sampling
Reliability
ISSN:0951-8320, 1879-0836
Online Access:Get full text
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Summary:The estimation of system failure probabilities may be a difficult task when the values involved are very small, so that sampling-based Monte Carlo methods may become computationally impractical, especially if the computer codes used to model the system response require large computational efforts, both in terms of time and memory. This paper proposes a modification of an algorithm proposed in literature for the efficient estimation of small failure probabilities, which combines FORM to an adaptive kriging-based importance sampling strategy (AK-IS). The modification allows overcoming an important limitation of the original AK-IS in that it provides the algorithm with the flexibility to deal with multiple failure regions characterized by complex, non-linear limit states. The modified algorithm is shown to offer satisfactory results with reference to four case studies of literature, outperforming in general several other alternative methods of literature. •We tackle low failure probability estimation within reliability analysis context.•We improve a kriging-based importance sampling for estimating failure probabilities.•The new algorithm is capable of dealing with multiple-disconnected failure regions.•The performances are better than other methods of literature on 4 test case-studies.
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ISSN:0951-8320
1879-0836
DOI:10.1016/j.ress.2014.06.023