| Title |
Evaluation of a Thermal Conductivity Prediction Model for Compacted Clay Based on a Machine Learning Method |
| Authors |
윤석(Yoon, Seok) ; 방현태(Bang, Hyun-Tae) ; 김건영(Kim, Geon-Young) ; 전해민(Jeon, Haemin) |
| DOI |
https://doi.org/10.12652/Ksce.2021.41.2.0123 |
| Keywords |
공학적 방벽;벤토나이트 완충재;열전도도;기계학습법 Engineered barrier system;Compacted bentonite;Thermal conductivity;Machine learning method |
| Abstract |
The buffer is a key component of an engineered barrier system that safeguards the disposal of high-level radioactive waste. Buffers are located between disposal canisters and host rock, and they can restrain the release of radionuclides and protect canisters from the inflow of ground water. Since considerable heat is released from a disposal canister to the surrounding buffer, the thermal conductivity of the buffer is a very important parameter in the entire disposal safety. For this reason, a lot of research has been conducted on thermal conductivity prediction models that consider various factors. In this study, the thermal conductivity of a buffer is estimated using the machine learning methods of: linear regression, decision tree, support vector machine (SVM), ensemble, Gaussian process regression (GPR), neural network, deep belief network, and genetic programming. In the results, the machine learning methods such as ensemble, genetic programming, SVM with cubic parameter, and GPR showed better performance compared with the regression model, with the ensemble with XGBoost and Gaussian process regression models showing best performance. |