Investigation on Thermal Conductivity of Soil Under Freeze–Thaw Action Based on Machine Learning Models
Thermal conductivity is a crucial factor for the soil, which is significantly affected by environmental conditions. Based on the variation in the thermal conductivity and the influencing factors of silty clay obtained by the freeze–thaw cycle test, this paper adopted four machine learning models opt...
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2025-02-01
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| author | Yuwei Chen Yadi Min Haiqiang Jiang Jing Luo Mengxin Liu Enliang Wang Xingchao Liu Ke Shi Xiaoqi Li |
| author_facet | Yuwei Chen Yadi Min Haiqiang Jiang Jing Luo Mengxin Liu Enliang Wang Xingchao Liu Ke Shi Xiaoqi Li |
| author_sort | Yuwei Chen |
| collection | DOAJ |
| description | Thermal conductivity is a crucial factor for the soil, which is significantly affected by environmental conditions. Based on the variation in the thermal conductivity and the influencing factors of silty clay obtained by the freeze–thaw cycle test, this paper adopted four machine learning models optimized by particle swarm optimization (PSO), including the artificial neural network model (ANN), random forest model (RF), support vector machine model (SVM), and extreme gradient boosting model (XGBoost) to predict the thermal conductivity of the soil. Meanwhile, mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient(R<sup>2</sup>) were used to evaluate the accuracy of the models. The accuracy of the machine learning model and empirical model were also compared. Then, the Monte Carlo simulation was used to analyze the stability of the models. The research results showed that the predicted performance of the machine learning models is significantly better than the empirical models. Among all the machine learning models, the R<sup>2</sup> of the PSO-ANN model is above 0.95, while both RMSE and MAE values are below 0.02 (W·m⁻¹·K⁻¹). In addition, the stability order of the machine learning models is PSO-XGBoost, PSO-ANN, PSO-RF, and PSO-SVM. Therefore, comprehensively considering the accuracy and stability of the four machine learning models, the PSO-ANN model is recommended to predict soil’s thermal conductivity under freeze–thaw action. |
| format | Article |
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| institution | DOAJ |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-02-01 |
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| spelling | doaj-art-e132de0aa5f44e0db705b3f29bfc54d52025-08-20T02:53:22ZengMDPI AGBuildings2075-53092025-02-0115575010.3390/buildings15050750Investigation on Thermal Conductivity of Soil Under Freeze–Thaw Action Based on Machine Learning ModelsYuwei Chen0Yadi Min1Haiqiang Jiang2Jing Luo3Mengxin Liu4Enliang Wang5Xingchao Liu6Ke Shi7Xiaoqi Li8State Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaSchool of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin 150030, ChinaState Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaState Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaNanjing University (Suzhou) High-Tech Institute, Suzhou 215000, ChinaSchool of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin 150030, ChinaSchool of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin 150030, ChinaSchool of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin 150030, ChinaSchool of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin 150030, ChinaThermal conductivity is a crucial factor for the soil, which is significantly affected by environmental conditions. Based on the variation in the thermal conductivity and the influencing factors of silty clay obtained by the freeze–thaw cycle test, this paper adopted four machine learning models optimized by particle swarm optimization (PSO), including the artificial neural network model (ANN), random forest model (RF), support vector machine model (SVM), and extreme gradient boosting model (XGBoost) to predict the thermal conductivity of the soil. Meanwhile, mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient(R<sup>2</sup>) were used to evaluate the accuracy of the models. The accuracy of the machine learning model and empirical model were also compared. Then, the Monte Carlo simulation was used to analyze the stability of the models. The research results showed that the predicted performance of the machine learning models is significantly better than the empirical models. Among all the machine learning models, the R<sup>2</sup> of the PSO-ANN model is above 0.95, while both RMSE and MAE values are below 0.02 (W·m⁻¹·K⁻¹). In addition, the stability order of the machine learning models is PSO-XGBoost, PSO-ANN, PSO-RF, and PSO-SVM. Therefore, comprehensively considering the accuracy and stability of the four machine learning models, the PSO-ANN model is recommended to predict soil’s thermal conductivity under freeze–thaw action.https://www.mdpi.com/2075-5309/15/5/750freeze–thaw actionmachine learning modelsoil’s thermal conductivityMonte Carlo simulationcold regions |
| spellingShingle | Yuwei Chen Yadi Min Haiqiang Jiang Jing Luo Mengxin Liu Enliang Wang Xingchao Liu Ke Shi Xiaoqi Li Investigation on Thermal Conductivity of Soil Under Freeze–Thaw Action Based on Machine Learning Models Buildings freeze–thaw action machine learning model soil’s thermal conductivity Monte Carlo simulation cold regions |
| title | Investigation on Thermal Conductivity of Soil Under Freeze–Thaw Action Based on Machine Learning Models |
| title_full | Investigation on Thermal Conductivity of Soil Under Freeze–Thaw Action Based on Machine Learning Models |
| title_fullStr | Investigation on Thermal Conductivity of Soil Under Freeze–Thaw Action Based on Machine Learning Models |
| title_full_unstemmed | Investigation on Thermal Conductivity of Soil Under Freeze–Thaw Action Based on Machine Learning Models |
| title_short | Investigation on Thermal Conductivity of Soil Under Freeze–Thaw Action Based on Machine Learning Models |
| title_sort | investigation on thermal conductivity of soil under freeze thaw action based on machine learning models |
| topic | freeze–thaw action machine learning model soil’s thermal conductivity Monte Carlo simulation cold regions |
| url | https://www.mdpi.com/2075-5309/15/5/750 |
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