Extensive studies have been conducted on thermal conductivity of bentonite buffer materials, as it affects the safety performance of barriers engineered to contain high-level radioactive waste. Bentonite is composed of several minerals, and studies have shown that the difference in the thermal conductivity of bentonites is due to the variation in their mineral composition. However, the specific reasons contributing to the difference, especially with regard to the thermal conductivity of bentonites with similar mineral composition, have not been elucidated. Therefore, in this study, bentonites with significantly different thermal conductivities, but of similar mineral compositions, are investigated. Most bentonites contain more than 60% of montmorillonite. Therefore, it is believed that the exchangeable cations of montmorillonite could affect the thermal conductivity of bentonites. The effect of bentonite type was comparatively analyzed and was verified through the effective medium model for thermal conductivity. Our results show that Ca-type bentonites have a higher thermal conductivity than Na-type bentonites.

In this study, the well-known non-destructive acoustic emission (AE) and electrical resistivity methods were employed to predict quantitative damage in the silo structure of the Wolsong Low and Intermediate Level Radioactive Waste Disposal Center (WLDC), Gyeongju, South Korea. Brazilian tensile test was conducted with a fully saturated specimen with a composition identical to that of the WLDC silo concrete. Bi-axial strain gauges, AE sensors, and electrodes were attached to the surface of the specimen to monitor changes. Both the AE hit and electrical resistance values helped in the anticipation of imminent specimen failure, which was further confirmed using a strain gauge. The quantitative damage (or damage variable) was defined according to the AE hits and electrical resistance and analyzed with stress ratio variations. Approximately 75% of the damage occurred when the stress ratio exceeded 0.5. Quantitative damage from AE hits and electrical resistance showed a good correlation (R = 0.988, RMSE = 0.044). This implies that AE and electrical resistivity can be complementarily used for damage assessment of the structure. In future, damage to dry and heated specimens will be examined using AE hits and electrical resistance, and the results will be compared with those from this study.

A safety assessment of radioactive waste repositories is a mandatory requirement process because there are possible radiological hazards owing to radionuclide migration from radioactive waste to the biosphere. For a reliable safety assessment, it is important to establish a parameter database that reflects the site-specific characteristics of the disposal facility and repository site. From this perspective, solubility, a major geochemical parameter, has been chosen as an important parameter for modeling the migration behavior of radionuclides. The solubilities were derived for Am, Ni, Tc, and U, which were major radionuclides in this study, and on-site groundwater data reflecting the operational conditions of the Gyeongju low and intermediate level radioactive waste (LILW) repository were applied to reflect the site-specific characteristics. The radiation dose was derived by applying the solubility and radionuclide inventory data to the RESRAD-OFFSITE code, and sensitivity analysis of the dose according to the solubility variation was performed. As a result, owing to the low amount of radionuclide inventory, the dose variation was insignificant. The derived solubility can be used as the main input data for the safety assessment of the Gyeongju LILW repository in the future.

Evaluating the quantitative damage to rocks through acoustic emission (AE) has become a research focus. Most studies mainly used one or two AE parameters to evaluate the degree of damage, but several AE parameters have been rarely used. In this study, several data-driven models were employed to reflect the combined features of AE parameters. Through uniaxial compression tests, we obtained mechanical and AE-signal data for five granite specimens. The maximum amplitude, hits, counts, rise time, absolute energy, and initiation frequency expressed as the cumulative value were selected as input parameters. The result showed that gradient boosting (GB) was the best model among the support vector regression methods. When GB was applied to the testing data, the root-mean-square error and R between the predicted and actual values were 0.96 and 0.077, respectively. A parameter analysis was performed to capture the parameter significance. The result showed that cumulative absolute energy was the main parameter for damage prediction. Thus, AE has practical applicability in predicting rock damage without conducting mechanical tests. Based on the results, this study will be useful for monitoring the near-field rock mass of nuclear waste repository.