Characteristics and Prediction of the Thermal Diffusivity of Sandy Soil

Baoming Dai, Yaxing Zhang, Haifeng Ding, Yunlong Xu and Zhiyun Liu
Additional contact information
Baoming Dai: China Railway Construction Investment Group Co., Ltd., Urumqi 830017, China
Yaxing Zhang: College of Geology Engineering and Geomatics, Chang’an University, Xi’an 710054, China
Haifeng Ding: China Railway Construction Investment Group Co., Ltd., Urumqi 830017, China
Yunlong Xu: China Railway Construction Investment Group Co., Ltd., Urumqi 830017, China
Zhiyun Liu: College of Geology Engineering and Geomatics, Chang’an University, Xi’an 710054, China

Energies, 2022, vol. 15, issue 4, 1-14

Abstract: 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.

Keywords: sandy soil; thermal diffusivity; RBF neural network; distribution characteristic; prediction model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/4/1524/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/4/1524/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:4:p:1524-:d:752710

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().