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 -