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http://dx.doi.org/10.7734/COSEIK.2021.34.2.77

Suggestions for Enhancing Sampling-Based Approach of Seismic Probabilistic Risk Assessment  

Kwag, Shinyoung (Department of Civil & Environmental Engineering, Hanbat National University)
Eem, Seunghyun (Department of Convergence and Fusion System Engineering, Kyungpook National University)
Choi, Eujeong (Structural Safety & Prognosis Research Division, Korea Atomic Energy Research Institute)
Ha, Jeong Gon (Structural Safety & Prognosis Research Division, Korea Atomic Energy Research Institute)
Hahm, Daegi (Structural Safety & Prognosis Research Division, Korea Atomic Energy Research Institute)
Publication Information
Journal of the Computational Structural Engineering Institute of Korea / v.34, no.2, 2021 , pp. 77-84 More about this Journal
Abstract
A sampling-based approach was devised as a nuclear seismic probabilistic risk assessment (SPRA) method to account for the partially correlated relationships between components. However, since this method is based on sampling, there is a limitation that a large number of samples must be extracted to estimate the results accurately. Thus, in this study, we suggest an effective approach to improve the existing sampling method. The main features of this approach are as follows. In place of the existing Monte Carlo sampling (MCS) approach, the Latin hypercube sampling (LHS) method that enables effective sampling in multiple dimensions is introduced to the SPRA method. In addition, the degree of segmentation of the seismic intensity is determined with respect to the final seismic risk result. By applying the suggested approach to an actual nuclear power plant as an example, the accuracy of the results were observed to be almost similar to those of the existing method, but the efficiency was increased by a factor of two in terms of the total number of samples extracted. In addition, it was confirmed that the LHS-based method improves the accuracy of the solution in a small sampling region.
Keywords
seismic risk; seismic fragility; sampling method; nuclear facility; nuclear power plant;
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