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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Vydáno v:	Reliability engineering & system safety Ročník 131; s. 109 - 117
Hlavní autoři:	Cadini, F., Santos, F., Zio, E.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Oxford Elsevier Ltd 01.11.2014 Elsevier
Témata:	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
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0951-8320 1879-0836
DOI:	10.1016/j.ress.2014.06.023