Correction Factors of the Exposure Scenario for Optimizing Radiation Protection During the Installation and Removal of the Steam Generator Nozzle Dams at Korean Pressurized Water Reactors

Scenario‐based dose assessment tools such as VRdose are widely used for radiological planning of high‐exposure maintenance tasks at nuclear power plants; however, these tools often yield conservative predictions that systematically exceed actual worker exposure. This study aims to quantify and interpret this discrepancy by deriving task‐specific correction factors for the installation and removal of steam generator nozzle dams at Korean pressurized water reactors. Ten years (2012–2021) of occupational exposure data reported in Korea Hydro and Nuclear Power annual dose reports were analyzed and compared with VRdose simulation results. To ensure a consistent comparison independent of workforce size and worker exposure time, both measured collective doses and simulated results were converted into time‐normalized dose rates. Annual correction factors were calculated as the ratio of actual to predicted dose rates, and their long‐term average value and statistical uncertainty were evaluated using 95% and 99% confidence intervals. The results show that VRdose consistently overpredicted worker exposure across all analyzed years. Predicted dose rates exceeded measured values by approximately a factor of 4–10. This systematic overestimation reflects conservative planning assumptions related to exposure time, radiation field representation, and shielding conditions rather than modeling deficiencies. The derived correction factors, therefore, quantify the inherent conservatism of the VRdose scenario for this specific high‐exposure task. Rather than serving as universal adjustment coefficients or deterministic predictors of future doses, the proposed correction factors provide interpretive planning references that allow radiation protection officers to contextualize scenario‐based predictions within realistic operational bounds. By integrating these factors into outage planning, more balanced ALARA‐driven decisions regarding manpower allocation, worker rotation, shielding, and task sequencing can be achieved. The methodology presented in this study offers a practical framework for transforming conservative scenario outputs into actionable radiological planning information and may be extended to other high‐exposure maintenance tasks through task‐ and unit‐specific implementation.