Determining the Thermal Conductivity of Clay during the Freezing Process by Artificial Neural Network
Thermal conductivity is an important thermal parameter in engineering design in cold regions. By measuring the thermal conductivity of clay using a transient hot-wire method in the laboratory, the influential factors of the thermal conductivity of soils during the freezing process were analyzed, and...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/5555565 |
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| author | Xiuling Ren Yanhui You Qihao Yu Guike Zhang Pan Yue Mingyang Jin |
| author_facet | Xiuling Ren Yanhui You Qihao Yu Guike Zhang Pan Yue Mingyang Jin |
| author_sort | Xiuling Ren |
| collection | DOAJ |
| description | Thermal conductivity is an important thermal parameter in engineering design in cold regions. By measuring the thermal conductivity of clay using a transient hot-wire method in the laboratory, the influential factors of the thermal conductivity of soils during the freezing process were analyzed, and a predictive model of thermal conductivity was developed with an artificial neural network (ANN) technology. The results show that the variation of thermal conductivity can be divided into three stages with decreasing temperature, positive temperature stage, transition stage, and negative temperature stage. The thermal conductivity increases sharply in the transition stage. The difference between the thermal conductivity at positive and negative temperature is small when the dry density of the soil specimens is larger than the critical dry density, while the difference is large if the dry density is less than the critical dry density. As the negative temperature decreases, the larger the moisture content of the soil specimens, the larger the increase of thermal conductivity. The effect of initial moisture content on thermal conductivity is more significant than that of dry density and temperature. The change tendency of the thermal conductivity calculated by the established ANN model is basically consistent with that of the laboratory-measured values, indicating that this model can be able to accurately predict the thermal conductivity of the soil specimens in the freezing process. |
| format | Article |
| id | doaj-art-764586b9642448a0ae7130ddc2177069 |
| institution | Kabale University |
| issn | 1687-8434 1687-8442 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-764586b9642448a0ae7130ddc21770692025-02-03T06:45:46ZengWileyAdvances in Materials Science and Engineering1687-84341687-84422021-01-01202110.1155/2021/55555655555565Determining the Thermal Conductivity of Clay during the Freezing Process by Artificial Neural NetworkXiuling Ren0Yanhui You1Qihao Yu2Guike Zhang3Pan Yue4Mingyang Jin5State Key Laboratory of Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, Gansu, ChinaState Key Laboratory of Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, Gansu, ChinaState Key Laboratory of Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, Gansu, ChinaYalong River Hydropower Development Company, Ltd, Chengdu 610065, Sichuan, ChinaYalong River Hydropower Development Company, Ltd, Chengdu 610065, Sichuan, ChinaState Key Laboratory of Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, Gansu, ChinaThermal conductivity is an important thermal parameter in engineering design in cold regions. By measuring the thermal conductivity of clay using a transient hot-wire method in the laboratory, the influential factors of the thermal conductivity of soils during the freezing process were analyzed, and a predictive model of thermal conductivity was developed with an artificial neural network (ANN) technology. The results show that the variation of thermal conductivity can be divided into three stages with decreasing temperature, positive temperature stage, transition stage, and negative temperature stage. The thermal conductivity increases sharply in the transition stage. The difference between the thermal conductivity at positive and negative temperature is small when the dry density of the soil specimens is larger than the critical dry density, while the difference is large if the dry density is less than the critical dry density. As the negative temperature decreases, the larger the moisture content of the soil specimens, the larger the increase of thermal conductivity. The effect of initial moisture content on thermal conductivity is more significant than that of dry density and temperature. The change tendency of the thermal conductivity calculated by the established ANN model is basically consistent with that of the laboratory-measured values, indicating that this model can be able to accurately predict the thermal conductivity of the soil specimens in the freezing process.http://dx.doi.org/10.1155/2021/5555565 |
| spellingShingle | Xiuling Ren Yanhui You Qihao Yu Guike Zhang Pan Yue Mingyang Jin Determining the Thermal Conductivity of Clay during the Freezing Process by Artificial Neural Network Advances in Materials Science and Engineering |
| title | Determining the Thermal Conductivity of Clay during the Freezing Process by Artificial Neural Network |
| title_full | Determining the Thermal Conductivity of Clay during the Freezing Process by Artificial Neural Network |
| title_fullStr | Determining the Thermal Conductivity of Clay during the Freezing Process by Artificial Neural Network |
| title_full_unstemmed | Determining the Thermal Conductivity of Clay during the Freezing Process by Artificial Neural Network |
| title_short | Determining the Thermal Conductivity of Clay during the Freezing Process by Artificial Neural Network |
| title_sort | determining the thermal conductivity of clay during the freezing process by artificial neural network |
| url | http://dx.doi.org/10.1155/2021/5555565 |
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