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Author Ni, P.H.; Jerez, D.J.; Fragkoulis, V.C.; Faes, M.G.R.; Valdebenito, M.A.; Beer, M.
Title (down) Operator Norm-Based Statistical Linearization to Bound the First Excursion Probability of Nonlinear Structures Subjected to Imprecise Stochastic Loading Type
Year 2022 Publication ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A-Civil Engineering Abbreviated Journal ASCE-ASME J. Risk Uncertain. Eng. Syst. A-Civ. Eng.
Volume 8 Issue 1 Pages 04021086
Keywords Uncertainty quantification; Imprecise probabilities; Operator norm theorem; Statistical linearization
Abstract This paper presents a highly efficient approach for bounding the responses and probability of failure of nonlinear models subjected to imprecisely defined stochastic Gaussian loads. Typically, such computations involve solving a nested double-loop problem, where the propagation of the aleatory uncertainty has to be performed for each realization of the epistemic parameters. Apart from near-trivial cases, such computation is generally intractable without resorting to surrogate modeling schemes, especially in the context of performing nonlinear dynamical simulations. The recently introduced operator norm framework allows for breaking this double loop by determining those values of the epistemic uncertain parameters that produce bounds on the probability of failure a priori. However, the method in its current form is only applicable to linear models due to the adopted assumptions in the derivation of the involved operator norms. In this paper, the operator norm framework is extended and generalized by resorting to the statistical linearization methodology to
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Notes WOS:000742414100022 Approved
Call Number UAI @ alexi.delcanto @ Serial 1550
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