We explain a Bayesian post-processing step for MCS. It is ideally suited to quantify the uncertainty and to evaluate credible intervals for the probability of failure.
For a given probability of failure, the variance and coefficient of variation of the Monte Carlo estimate can be evaluated analytically. From this, the total number of samples required to maintain a target coefficient of variation can be deduced.
Monte Carlo simulation is a very robust structural reliability method because its performance depends solely on the total number of samples and the underlying probability of failure.
Monte Carlo simulation divides the number of samples with system failure by the total number of random samples generated to estimate the probability of failure in a reliability analysis.
The limit state function distinguishes undesired from acceptable system behavior. It is used in structural reliability analysis to define a failure criterion.
Structural reliability analysis aims at evaluating the probability of failure of a structure or system. To evaluate the probability of failure an integral over the sample space must be computed.
