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http://dx.doi.org/10.12652/Ksce.2021.41.2.0123

Evaluation of a Thermal Conductivity Prediction Model for Compacted Clay Based on a Machine Learning Method  

Yoon, Seok (Korea Atomic Energy Research Institute)
Bang, Hyun-Tae (Hanbat National Univesity)
Kim, Geon-Young (Korea Atomic Energy Research Institute)
Jeon, Haemin (Hanbat National Univesity)
Publication Information
KSCE Journal of Civil and Environmental Engineering Research / v.41, no.2, 2021 , pp. 123-131 More about this Journal
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.
Keywords
Engineered barrier system; Compacted bentonite; Thermal conductivity; Machine learning method;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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