Hamiltonian Monte Carlo methods for Subset Simulation in reliability analysis

Reliability sensitivity analysis with Monte Carlo methods

In many practical applications of structural reliability analysis, one is interested in knowing the sensitivity of the probability of failure to design parameters that enter the definition of the limit-state function. This information is required for example in reliability-based design optimization. Parameter sensitivities are obtained by FORM/SORM, in terms of the sensitivity of the respective...

Subset Simulation and Line Sampling for Advanced Monte Carlo Reliability Analysis

In this paper, the recently developed Subset Simulation (SS) and Line Sampling (LS) tech niques are considered for improving the efficiency of Monte Carlo Simulation (MCS) in the estimation of sys tem failure probability. The SS method is founded on the idea that a small failure probability can be expressed as a product of larger conditional probabilities of some intermediate events: with a pro...

Methods of Monte Carlo Simulation

Structural reliability analysis using Monte Carlo simulation and neural networks

This paper examines a methodology for computing the probability of structural failure by combining neural networks (NN) and Monte Carlo simulation (MCS). MCS is a very powerful tool, simple to implement and capable of solving a broad range of reliability problems. However, its use for evaluation of very low probabilities of failure, of the order of magnitude currently found in structural reliab...

Reliability Estimation by Advanced Monte Carlo Simulation

Monte Carlo Simulation (MCS) offers a powerful means for evaluating the reliability of a system, due to the modeling flexibility that it offers indifferently of the type and dimension of the problem. The method is based on the repeated sampling of realizations of system configurations, which however are seldom of failure so that a large number of realizations must be simulated in order to achie...