Details
| Original language | English |
|---|---|
| Pages (from-to) | 49-65 |
| Number of pages | 17 |
| Journal | Structural safety |
| Volume | 29 |
| Issue number | 1 |
| Early online date | 13 Mar 2006 |
| Publication status | Published - Jan 2007 |
| Externally published | Yes |
Abstract
In this paper a novel technique for random vector sampling starting from rare data are presented. This model-free sampling technique is developed to operate without a probabilistic model. Instead of estimating a distribution function, the information contained in a given small sample is extracted directly to produce the sampling result as a second sample of considerably larger size that completely reflects the properties of the original small sample. As a further enhancement, the new sampling technique is extended to processing imprecise data. Model-free sampling can be coupled to stochastic structural analysis and safety assessment by application to input data or to result data. In the case of limited data, for instance, due to a high numerical cost of the underlying computational model, the novel technique can be applied to generate a proper estimation of stochastic structural responses and, thanks to a sound reproduction of distribution tails, of structural reliability. In this context it can provide a basis for increasing the numerical efficiency of Monte-Carlo simulations in computational stochastic mechanics. The usefulness of the model-free sampling technique is underlined by means of numerical examples.
Keywords
- Fuzzy randomness, Imprecise data, Monte-Carlo simulation, Safety assessment, Sampling, Uncertain structural analysis
ASJC Scopus subject areas
- Engineering(all)
- Civil and Structural Engineering
- Engineering(all)
- Building and Construction
- Engineering(all)
- Safety, Risk, Reliability and Quality
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In: Structural safety, Vol. 29, No. 1, 01.2007, p. 49-65.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Model-free sampling
AU - Beer, Michael
N1 - Funding information: The author gratefully acknowledges the support of the German Research Foundation (DFG) and of the Alexander von Humboldt-Foundation (AvH).
PY - 2007/1
Y1 - 2007/1
N2 - In this paper a novel technique for random vector sampling starting from rare data are presented. This model-free sampling technique is developed to operate without a probabilistic model. Instead of estimating a distribution function, the information contained in a given small sample is extracted directly to produce the sampling result as a second sample of considerably larger size that completely reflects the properties of the original small sample. As a further enhancement, the new sampling technique is extended to processing imprecise data. Model-free sampling can be coupled to stochastic structural analysis and safety assessment by application to input data or to result data. In the case of limited data, for instance, due to a high numerical cost of the underlying computational model, the novel technique can be applied to generate a proper estimation of stochastic structural responses and, thanks to a sound reproduction of distribution tails, of structural reliability. In this context it can provide a basis for increasing the numerical efficiency of Monte-Carlo simulations in computational stochastic mechanics. The usefulness of the model-free sampling technique is underlined by means of numerical examples.
AB - In this paper a novel technique for random vector sampling starting from rare data are presented. This model-free sampling technique is developed to operate without a probabilistic model. Instead of estimating a distribution function, the information contained in a given small sample is extracted directly to produce the sampling result as a second sample of considerably larger size that completely reflects the properties of the original small sample. As a further enhancement, the new sampling technique is extended to processing imprecise data. Model-free sampling can be coupled to stochastic structural analysis and safety assessment by application to input data or to result data. In the case of limited data, for instance, due to a high numerical cost of the underlying computational model, the novel technique can be applied to generate a proper estimation of stochastic structural responses and, thanks to a sound reproduction of distribution tails, of structural reliability. In this context it can provide a basis for increasing the numerical efficiency of Monte-Carlo simulations in computational stochastic mechanics. The usefulness of the model-free sampling technique is underlined by means of numerical examples.
KW - Fuzzy randomness
KW - Imprecise data
KW - Monte-Carlo simulation
KW - Safety assessment
KW - Sampling
KW - Uncertain structural analysis
UR - http://www.scopus.com/inward/record.url?scp=33749058526&partnerID=8YFLogxK
U2 - 10.1016/j.strusafe.2006.01.001
DO - 10.1016/j.strusafe.2006.01.001
M3 - Article
AN - SCOPUS:33749058526
VL - 29
SP - 49
EP - 65
JO - Structural safety
JF - Structural safety
SN - 0167-4730
IS - 1
ER -