Machine learning-based prediction of the thermal conductivity of filling material incorporating steelmaking slag in a ground heat exchanger system
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...
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| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-89713-7 |
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| author | Gyeong-o Kang Young-sang Kim Jung-goo Kang Seongkyu Chang |
| author_facet | Gyeong-o Kang Young-sang Kim Jung-goo Kang Seongkyu Chang |
| author_sort | Gyeong-o Kang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4bc5880545cb4ce59fca38f4393a5df9 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-4bc5880545cb4ce59fca38f4393a5df92025-08-20T02:17:13ZengNature PortfolioScientific Reports2045-23222025-04-0115111210.1038/s41598-025-89713-7Machine learning-based prediction of the thermal conductivity of filling material incorporating steelmaking slag in a ground heat exchanger systemGyeong-o Kang0Young-sang Kim1Jung-goo Kang2Seongkyu Chang3Department of Civil Engineering, Gwangju UniversityDepartment of Civil Engineering, Chonnam National UniversityDepartment of Civil Engineering, Gwangju UniversityDepartment of Civil Engineering, Gwangju UniversityAbstract 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.https://doi.org/10.1038/s41598-025-89713-7Thermal conductivityMachine learningSteelmaking slagHeat-transfer mediumGround heat exchanger |
| spellingShingle | Gyeong-o Kang Young-sang Kim Jung-goo Kang Seongkyu Chang Machine learning-based prediction of the thermal conductivity of filling material incorporating steelmaking slag in a ground heat exchanger system Scientific Reports Thermal conductivity Machine learning Steelmaking slag Heat-transfer medium Ground heat exchanger |
| title | Machine learning-based prediction of the thermal conductivity of filling material incorporating steelmaking slag in a ground heat exchanger system |
| title_full | Machine learning-based prediction of the thermal conductivity of filling material incorporating steelmaking slag in a ground heat exchanger system |
| title_fullStr | Machine learning-based prediction of the thermal conductivity of filling material incorporating steelmaking slag in a ground heat exchanger system |
| title_full_unstemmed | Machine learning-based prediction of the thermal conductivity of filling material incorporating steelmaking slag in a ground heat exchanger system |
| title_short | Machine learning-based prediction of the thermal conductivity of filling material incorporating steelmaking slag in a ground heat exchanger system |
| title_sort | machine learning based prediction of the thermal conductivity of filling material incorporating steelmaking slag in a ground heat exchanger system |
| topic | Thermal conductivity Machine learning Steelmaking slag Heat-transfer medium Ground heat exchanger |
| url | https://doi.org/10.1038/s41598-025-89713-7 |
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