Confidence Bounds on Structural Reliability

Mehta, S. R.; Cruse, T. A.; Mahadevan, Sankaran

doi:10.2514/6.1993-1377

Cited by 4 publications

(2 citation statements)

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“…Individual developments concern, for example, approaches of info-gap uncertainty [32], robust design with convex models [33] and with interval arithmetics [34]. In [35] an interval based method for structural reliability estimation including confidence intervals for input distribution parameters is presented. A sophisticated sampling-based approach in an evidence theory framework from the related field of sensitivity analysis is provided in [10].…”

Section: Problem Specificationmentioning

confidence: 99%

Designing robust structures – A nonlinear simulation based approach

Beer

Liebscher

2008

Computers & Structures

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Section: Problem Specificationmentioning

confidence: 99%

Designing robust structures – A nonlinear simulation based approach

Beer

Liebscher

2008

Computers & Structures

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“…Other authors have quantified the uncertainty in the probability of failure due to uncertainty in the random variable moments using the first order reliability method (FORM) (Der Kiureghian and Liu 1986;Torng and Thacker 1992;Torng and Thacker 1993;Mehta, Cruse, and Mahadevan 1993;Venkataraman et al 2007;McDonald, Zaman, and Mahadevan 2009). FORM estimates the minimum distance, known as reliability index, from the origin of a standard normal variable space to the limit state.…”

Section: Introductionmentioning

confidence: 99%

Optimal allocation of testing resources for statistical simulations

Quintana

Millwater

Singh

et al. 2014

Engineering Optimization

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Statistical estimates from simulation involve uncertainty caused by the variability in the input random variables due to limited data. Allocating resources to obtain more experimental data of the input variables to better characterize their probability distributions can reduce the variance of statistical estimates. The methodology proposed determines the optimal number of additional experiments required to minimize the variance of the output moments given single or multiple constraints. The method uses multivariate t-distribution and Wishart distribution to generate realizations of the population mean and covariance of the input variables, respectively, given an amount of available data. This method handles independent and correlated random variables. A particle swarm method is used for the optimization. The optimal number of additional experiments per variable depends on the number and variance of the initial data, the influence of the variable in the output function and the cost of each additional experiment. The methodology is demonstrated using a fretting fatigue example.

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