Details
| Original language | English |
|---|---|
| Pages (from-to) | 170-182 |
| Number of pages | 13 |
| Journal | Structural Safety |
| Volume | 52 |
| Issue number | Part B |
| Early online date | 15 Nov 2014 |
| Publication status | Published - Jan 2015 |
| Externally published | Yes |
Abstract
A numerical strategy for the efficient estimation of set-valued failure probabilities, coupling Monte Carlo with optimization methods, is presented in this paper. The notion of uncertainty is generalized to include both aleatory and epistemic uncertainties, allowing to capture gaps of knowledge and scarcity of data. The proposed formulation of the generalized uncertainty model allows for sets of probability distribution functions, also known as credal sets, and sets of bounded variables. The developed Advanced Line Sampling method is combined with the generalized uncertainty model, in order to both speed up the reliability analysis, and provide a better estimate for the lower and upper bounds of the failure probability. The proposed strategy knocks down the computational barrier of computing interval failure probabilities, and reduces the cost of a robust reliability analysis by many orders of magnitude. The efficiency and applicability of the developed method is demonstrated via numerical examples. The solution strategy is integrated into the open-source software for uncertainty quantification and risk analysis O. penC. ossan, allowing its application on large-scale engineering problems as well as broadening its spectrum of applications.
Keywords
- Aleatory and epistemic uncertainty, Credal sets, Failure probability, Generalized uncertainty model, Line Sampling, Monte Carlo
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. 52, No. Part B, 01.2015, p. 170-182.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Advanced Line Sampling for efficient robust reliability analysis
AU - de Angelis, Marco
AU - Patelli, Edoardo
AU - Beer, Michael
PY - 2015/1
Y1 - 2015/1
N2 - A numerical strategy for the efficient estimation of set-valued failure probabilities, coupling Monte Carlo with optimization methods, is presented in this paper. The notion of uncertainty is generalized to include both aleatory and epistemic uncertainties, allowing to capture gaps of knowledge and scarcity of data. The proposed formulation of the generalized uncertainty model allows for sets of probability distribution functions, also known as credal sets, and sets of bounded variables. The developed Advanced Line Sampling method is combined with the generalized uncertainty model, in order to both speed up the reliability analysis, and provide a better estimate for the lower and upper bounds of the failure probability. The proposed strategy knocks down the computational barrier of computing interval failure probabilities, and reduces the cost of a robust reliability analysis by many orders of magnitude. The efficiency and applicability of the developed method is demonstrated via numerical examples. The solution strategy is integrated into the open-source software for uncertainty quantification and risk analysis O. penC. ossan, allowing its application on large-scale engineering problems as well as broadening its spectrum of applications.
AB - A numerical strategy for the efficient estimation of set-valued failure probabilities, coupling Monte Carlo with optimization methods, is presented in this paper. The notion of uncertainty is generalized to include both aleatory and epistemic uncertainties, allowing to capture gaps of knowledge and scarcity of data. The proposed formulation of the generalized uncertainty model allows for sets of probability distribution functions, also known as credal sets, and sets of bounded variables. The developed Advanced Line Sampling method is combined with the generalized uncertainty model, in order to both speed up the reliability analysis, and provide a better estimate for the lower and upper bounds of the failure probability. The proposed strategy knocks down the computational barrier of computing interval failure probabilities, and reduces the cost of a robust reliability analysis by many orders of magnitude. The efficiency and applicability of the developed method is demonstrated via numerical examples. The solution strategy is integrated into the open-source software for uncertainty quantification and risk analysis O. penC. ossan, allowing its application on large-scale engineering problems as well as broadening its spectrum of applications.
KW - Aleatory and epistemic uncertainty
KW - Credal sets
KW - Failure probability
KW - Generalized uncertainty model
KW - Line Sampling
KW - Monte Carlo
UR - http://www.scopus.com/inward/record.url?scp=84918586942&partnerID=8YFLogxK
U2 - 10.1016/j.strusafe.2014.10.002
DO - 10.1016/j.strusafe.2014.10.002
M3 - Article
AN - SCOPUS:84918586942
VL - 52
SP - 170
EP - 182
JO - Structural Safety
JF - Structural Safety
SN - 0167-4730
IS - Part B
ER -