Realistic and efficient reliability estimation for aerospace structures

Reliability Estimation by Advanced Monte Carlo Simulation

“…The underlying idea is to employ lines instead of random points in order to probe the failure domain of the high-dimensional system under analysis (Pradlwarter et al 2005).…”

“…In this respect, effective approaches are offered by Subset Simulation (SS) (Au and Beck 2001;Au and Beck 2003b) and Line Sampling (LS) Pradlwarter et al 2005).…”

“…An "important direction" is optimally determined to point towards the failure domain of interest and a number of conditional, one-dimensional problems are solved along such direction, in place of the high-dimensional problem (Pradlwarter et al 2005). The approach has been shown to perform always better than standard MCS; furthermore, if the boundaries of the failure domain of interest are not too rough (i.e., almost linear) and the "important direction" is almost perpendicular to them, the variance of the failure probability estimator could be ideally reduced to zero .…”

“…In the LS method, lines, instead of random points, are used to probe the failure domain of the high-dimensional problem under analysis (Pradlwarter et al 2005). An "important direction" is optimally determined to point towards the failure domain of interest and a number of conditional, one-dimensional problems are solved along such direction, in place of the high-dimensional problem (Pradlwarter et al 2005).…”

Overturning Probability of Railway Vehicles under Wind Gust Loads

“…Here, all distributions are mapped to a standard Gaussian space, in which the shortest distance to the failure domain, the so called design point, is computed. After that, importance sampling around the design point and line sampling [16] has been employed in order to obtain reliable estimates of the conditional failure probability.…”