Low to intermediate radioactive waste disposal concrete structures are subjected to coupled hydromechanical conditions and the identification of structural damage is crucial to ensure safe long-term disposal. Different damage models for concrete and the surrounding rock can affect the damage characteristics of radioactive waste disposal structures. In this study, the effects of different rock damage models are applied to the hydro-mechanical-damage coupled structural analysis of the Wolseong Low and Intermediate Level Radioactive Waste Disposal Center silo. A two-dimensional model of the disposal silo was modeled using the finite element analysis software COMSOL and the Mazars’ damage model was applied to the silo concrete. The Mazars’ model parameters were obtained from uniaxial compression and tensile tests on cylindrical concrete specimens after 28 days of water curing and further 32 days of wet curing at 75°C). The COMSOL embedded Richards equation module was used to simulate hydraulic analysis. Structural loading due to waste disposal was applied at the bottom of the silo structure and the damage evolution characteristics were investigated. The non-linear mechanical rock behavior obtained from laboratory tests (Hoek-Brown criterion, resonant column test, Mazar’s damage model) and field tests (Goodman Jack) were input to assess the effects of different rock damage models. The results highlight the importance of structural damage consideration when assessing the long-term stability and safety of underground radioactive waste disposal structures under coupled hydro-mechanical conditions.

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.