ESTIMATION OF UNCERTAINTY CHANGE OF RELIABILITY IN ADAPTIVE SAMPLING UNDER PREDICTION UNCERTAINTY OF GAUSSIAN PROCESS

A novel approach is introduced to estimate the change in the variance of the probability of failure by adding a sample to the Gaussian process (GP) in a conservative manner. Uncertainty in probability stems from prediction uncertainty and GP is used to represent the uncertainty. In the estimation of variance, a single-loop Monte Carlo Simulation (MCS) alleviates the computational burden. The result shows that the proposed methodology well predicts the change by a sample, maintaining the conservativeness by ignoring correlation in GP, yet the computational cost is at the same level as single-loop MCS.