Feasibility study of progressive Latin hypercube sampling and quasi-Monte Carlo simulation for probabilistic risk assessment

In probabilistic risk assessment (PRA), two main methods exist for quantifying fault trees: theoretical and empirical (sampling). The efficiency of PRA quantification varies depending on the sampling method used. This study evaluated the feasibility of using quasi-Monte Carlo simulation (Quasi-MCS) and progressive Latin hypercube sampling (P-LHS) for PRA quantification. Eight risk outcomes were derived through PRA for internal and external events in four cases. The PRA convergence, variability, and error rates of each sampling method were compared and analyzed. The comparison analysis revealed that all sampling methods had an error rate of approximately 2% with 9,000 total samples. P-LHS exhibited the best convergence and variability among the methods, followed by Quasi-MCS and LHS. Although Quasi-MCS showed more significant variability than LHS as the number of events increased, its error rate remained within 2% with 9,000 samples. Therefore, both P-LHS and Quasi-MCS are feasible for PRA quantification.