The ,Aspoe Pillar Stability Experiment （APSE） is an in situ experiment for investigating the spalling mechanism under mechanical and thermal loading conditions in a crystalline rock. In this study, the thermo-mechan...The ,Aspoe Pillar Stability Experiment （APSE） is an in situ experiment for investigating the spalling mechanism under mechanical and thermal loading conditions in a crystalline rock. In this study, the thermo-mechanical behaviors in the APSE were investigated with three models： （1） a Full model with rough meshes for calculating the influence of tunnel excavation; （2） a Submodel with fine meshes for predicting the thermo-mechanical behavior in the pillar during the borehole drilling, heating, and cool- ing phases; and （3） a Thin model for modeling the effect of slot cutting for de-stressing around the pillar. In order to import the stresses calculated from the Full model to the Submodel and to define the complex thermal boundary conditions, artificial neural networks （NNs） were utilized. From this study, it was pos- sible to conclude that the stepwise approach with the application of NNs was useful for predicting the complex response of the pillar under severe thermo-mechanical loading conditions.展开更多
基金within the context of the international DECOVALEX Project (DEvelopment of COupled models and their VALidation against EXperiments)supported by Korea Atomic Energy Research Institute (KAERI) as one of the Funding Organizations of the project,through the Nuclear Research and Development Program of KOSEF with a grant funded by MEST+3 种基金supported by Inha University Research Grant (INHA-44095-1)the support by Seoul National University (SNU)Swedish Nuclear Fuel and Waste Management Co. (SKB), Swedenprovided by SKB through its sp Pillar Stability Experiment project
文摘The ,Aspoe Pillar Stability Experiment （APSE） is an in situ experiment for investigating the spalling mechanism under mechanical and thermal loading conditions in a crystalline rock. In this study, the thermo-mechanical behaviors in the APSE were investigated with three models： （1） a Full model with rough meshes for calculating the influence of tunnel excavation; （2） a Submodel with fine meshes for predicting the thermo-mechanical behavior in the pillar during the borehole drilling, heating, and cool- ing phases; and （3） a Thin model for modeling the effect of slot cutting for de-stressing around the pillar. In order to import the stresses calculated from the Full model to the Submodel and to define the complex thermal boundary conditions, artificial neural networks （NNs） were utilized. From this study, it was pos- sible to conclude that the stepwise approach with the application of NNs was useful for predicting the complex response of the pillar under severe thermo-mechanical loading conditions.
