Characteristics and Prediction of the Thermal Diffusivity of Sandy Soil
Revealing the variation law of thermal diffusivity of sandy soil can provide a theoretical basis for the engineering design and construction in cold and arid regions. Based on experimental data of sandy soil samples, the distribution characteristics and influence of dry density and moisture content...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/4/1524 |
| _version_ | 1797480679434354688 |
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| author | Baoming Dai Yaxing Zhang Haifeng Ding Yunlong Xu Zhiyun Liu |
| author_facet | Baoming Dai Yaxing Zhang Haifeng Ding Yunlong Xu Zhiyun Liu |
| author_sort | Baoming Dai |
| collection | DOAJ |
| description | Revealing the variation law of thermal diffusivity of sandy soil can provide a theoretical basis for the engineering design and construction in cold and arid regions. Based on experimental data of sandy soil samples, the distribution characteristics and influence of dry density and moisture content on thermal diffusivity are analyzed in this work. Then, the prediction model based on the empirical fitting formula and RBF neural network method for thermal diffusivity of frozen and unfrozen sandy soil is established, and the prediction accuracy of different prediction methods is compared. The results show that (1) thermal diffusivity of sandy soil is positively correlated with the particle size. With the increase of sand size, thermal diffusivity of sandy soil increases significantly. (2) Partial correlation among natural moisture content, dry density, and thermal diffusivity varies with different frozen and unfrozen conditions. (3) For unfrozen sandy soil, the binary RBF neural network prediction model is obviously better than that of the binary empirical fitting formula model. (4) The ternary prediction model has significantly higher prediction accuracy than that of the binary prediction model for frozen sandy soil, and the ternary RBF neural network model has the best prediction effect among the four methods. |
| first_indexed | 2024-03-09T22:03:32Z |
| format | Article |
| id | doaj.art-6ec664e133c448ef929f02db8429bf78 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T22:03:32Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-6ec664e133c448ef929f02db8429bf782023-11-23T19:45:30ZengMDPI AGEnergies1996-10732022-02-01154152410.3390/en15041524Characteristics and Prediction of the Thermal Diffusivity of Sandy SoilBaoming Dai0Yaxing Zhang1Haifeng Ding2Yunlong Xu3Zhiyun Liu4China Railway Construction Investment Group Co., Ltd., Urumqi 830017, ChinaCollege of Geology Engineering and Geomatics, Chang’an University, Xi’an 710054, ChinaChina Railway Construction Investment Group Co., Ltd., Urumqi 830017, ChinaChina Railway Construction Investment Group Co., Ltd., Urumqi 830017, ChinaCollege of Geology Engineering and Geomatics, Chang’an University, Xi’an 710054, ChinaRevealing the variation law of thermal diffusivity of sandy soil can provide a theoretical basis for the engineering design and construction in cold and arid regions. Based on experimental data of sandy soil samples, the distribution characteristics and influence of dry density and moisture content on thermal diffusivity are analyzed in this work. Then, the prediction model based on the empirical fitting formula and RBF neural network method for thermal diffusivity of frozen and unfrozen sandy soil is established, and the prediction accuracy of different prediction methods is compared. The results show that (1) thermal diffusivity of sandy soil is positively correlated with the particle size. With the increase of sand size, thermal diffusivity of sandy soil increases significantly. (2) Partial correlation among natural moisture content, dry density, and thermal diffusivity varies with different frozen and unfrozen conditions. (3) For unfrozen sandy soil, the binary RBF neural network prediction model is obviously better than that of the binary empirical fitting formula model. (4) The ternary prediction model has significantly higher prediction accuracy than that of the binary prediction model for frozen sandy soil, and the ternary RBF neural network model has the best prediction effect among the four methods.https://www.mdpi.com/1996-1073/15/4/1524sandy soilthermal diffusivityRBF neural networkdistribution characteristicprediction model |
| spellingShingle | Baoming Dai Yaxing Zhang Haifeng Ding Yunlong Xu Zhiyun Liu Characteristics and Prediction of the Thermal Diffusivity of Sandy Soil Energies sandy soil thermal diffusivity RBF neural network distribution characteristic prediction model |
| title | Characteristics and Prediction of the Thermal Diffusivity of Sandy Soil |
| title_full | Characteristics and Prediction of the Thermal Diffusivity of Sandy Soil |
| title_fullStr | Characteristics and Prediction of the Thermal Diffusivity of Sandy Soil |
| title_full_unstemmed | Characteristics and Prediction of the Thermal Diffusivity of Sandy Soil |
| title_short | Characteristics and Prediction of the Thermal Diffusivity of Sandy Soil |
| title_sort | characteristics and prediction of the thermal diffusivity of sandy soil |
| topic | sandy soil thermal diffusivity RBF neural network distribution characteristic prediction model |
| url | https://www.mdpi.com/1996-1073/15/4/1524 |
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