Uncertainty Quantification in Fault Tree Analysis: Estimating Business Interruption due to Seismic Hazard

Prabhu, Saurabh; Ehrett, Carl; Javanbarg, Mohammad; Brown, D. Andrew; Lehmann, Marc; Atamturktur, Sez

doi:10.1061/(asce)nh.1527-6996.0000360

Cited by 2 publications

(2 citation statements)

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“…Still, multi-hazard assessments are at their budding stage when compared to the single-hazard risk assessments [3]. In these circumstances, a Boolean method is adopted for both single-and multi-hazard risk quantification for systems in various research domains [9][10][11]. With this method, accurate probabilities of system failure can be evaluated with a relatively simple form.…”

Section: Introductionmentioning

confidence: 99%

“…For example, in the structural engineering domain, Salman and Li described the system performance of the power-grid in Boolean form to determine the effectiveness of a multi-hazard (i.e., earthquake and hurricane) risk mitigation plan [9]. In addition, for hazard management, Prabhu et al [10,11] also used a Boolean approach to model the system performance of a business under both single [10] and multiple hazards [11].…”

Section: Introductionmentioning

confidence: 99%

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Development of a Two-Stage DQFM to Improve Efficiency of Single- and Multi-Hazard Risk Quantification for Nuclear Facilities

Choi

Kwag

et al. 2021

Energies

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The probabilistic safety assessment (PSA) of a nuclear power plant (NPP) under single and multiple hazards is one of the most important tasks for disaster risk management of nuclear facilities. To date, various approaches—including the direct quantification of the fault tree using the Monte Carlo simulation (DQFM) method—have been employed to quantify single- and multi-hazard risks to nuclear facilities. The major advantage of the DQFM method is its applicability to a partially correlated system. Other methods can represent only an independent or a fully correlated system, but DQFM can quantify the risk of partially correlated system components by the sampling process. However, as a sampling-based approach, DQFM involves computational costs which increase as the size of the system and the number of hazards increase. Therefore, to improve the computational efficiency of the conventional DQFM, a two-stage DQFM method is proposed in this paper. By assigning enough samples to each hazard point according to its contribution to the final risk, the proposed two-stage DQFM can effectively reduce computational costs for both single- and multi-hazard risk quantification. Using examples of single- and multi-hazard threats to nuclear facilities, the effectiveness of the proposed two-stage DQFM is successfully demonstrated. Especially, two-stage DQFM saves computation time of conventional DQFM up to 72% for multi-hazard example.

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