Machine learning-based prediction of the thermal conductivity of filling material incorporating steelmaking slag in a ground heat exchanger system - Gyeong-o Kang; Young-sang Kim; Jung-goo Kang; Seongkyu Chang (Scientific Reports)
Abstract This study used machine learning models to predict the thermal conductivity of heat-transfer materials based on steelmaking slag. A dataset containing various physical parameters of the heat-transfer materials was obtained from previous research results and Pearson correlation analysis was used to select the optimal input variable. Three machine learning models—support vector regression (SVR), random forest (RF), and multilayer perceptron (MLP)—were assessed to determine the most accurate model for predicting the thermal conductivity of the heat-transfer materials. K-fold cross-validation was applied to each model to prevent overfitting of the results and to generalize the prediction models. All three models predicted the thermal conductivity better than a previous empirical method. The SVR model exhibited the best prediction accuracy across the whole dataset, confirming that this model can provide a simple and practical method for predicting the thermal conductivity of reinforced soil without the need for time-consuming and expensive experiments. Finally, equations based on SVR were proposed that can predict thermal conductivity under different experimental and material conditions.