Investigation of Temperature Variation Characteristics and a Prediction Model of Sandy Soil Thermal Conductivity in the Near-Phase-Transition Zone
Source abstract
Abstract
Soil thermal conductivity in the near-phase-transition zone is a key parameter affecting the thermal stability of permafrost engineering and its catastrophic thermal processes. Therefore, accurately determining the soil thermal conductivity in this specific temperature zone has important theoretical and engineering significance. In the present work, a method for testing the thermal conductivity of fine sandy soil in the near-phase-transition zone was proposed by measuring thermal conductivity with the transient plane heat source method and determining the volumetric specific heat capacity by weighing unfrozen water contents. The unfrozen water content of sand specimens in the near-phase-transition zone was tested, and a corresponding empirical fitting formula was established. Finally, based on the testing results, temperature variation trends and parameter influence laws of thermal conductivity in the near-phase-transition zone were analyzed, and thermal conductivity prediction models based on multiple regression (MR) and a radial basis function neural network (RBFNN) were also established. The results show the following: (1) The average error of the proposed test method in this work and the reference steady-state heat flow method is only 7.25%, which validates the reliability of the proposed test method. (2) The variation in unfrozen water contents in fine sandy soil in the range of 0~−3 °C accounts for over 80% of the variation in the entire negative temperature range. The unfrozen water content and thermal conductivity curves exhibit a similar trend, and the near-phase-transition zone can be divided into a drastic phase transition zone and a stable phase transition zone. (3) Increases in the thermal conductivity of fine sandy soil mainly occur the drastic phase transition zone, where these increases account for about 60% of the total increase in thermal conductivity in the entire negative temperature region. With the increase in density and total water content, the rate of increase in thermal conductivity in the drastic phase transition zone gradually decreases. (4) The R2, MAE, and RSME of the RBFNN model in the drastic phase transition zone are 0.991, 0.011, and 0.021, respectively, which are better than those of the MR prediction model.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Geothermal Energy Systems and Applications · Climate change and permafrost · Soil and Unsaturated Flow
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Geology Engineering and Geomatics, Chang’an University, Xi’an 710054, China
Funding
National Science Foundation of China · Open fund of National Key Laboratory of Green and Long-Life Road Engineering in Extreme Environment · Scientific Innovation Practice Project of Postgraduates of Chang’an University
References (46)
Show source-provided references
1. Qin, D.H. (2018). Introduction to Cryosphere Science, Science Press.
2. Cryospheric Climatology: Emerging Branch of Cryospheric Science
3. Review: Progress in permafrost hydrogeology in China10.1007/s10040-018-1802-6 ↗
4. Research progress of the thermophysical and mechanical properties of concrete subjected to freeze-thaw cycles10.1016/j.conbuildmat.2022.127254 ↗
5. Engineering in the rugged permafrost terrain on the roof of the world under a warming climate10.1002/ppp.2059 ↗
6. Research progress of soil thermal conductivity and its predictive models10.1016/j.coldregions.2023.104027 ↗
7. Express highway embankment distress and occurring probability in permafrost regions on the Qinghai-Tibet Plateau10.1016/j.trgeo.2023.101069 ↗
8. Quantifying the effect of a retrogressive thaw slump on soil freeze-thaw erosion in permafrost regions on the Qinghai-Tibet Plateau10.1002/ldr.4631 ↗
9. Non-stationary random vibration analysis of railway embankments in permafrost regions under train loads using the explicit time-domain method
10. Engineering risk analysis in cold regions: State of the art and perspectives10.1016/j.coldregions.2019.102963 ↗
11. Xu, X., Wang, J., and Zhang, L. (2001). Physics of Frozen Soil, Science Press.
12. Laboratory techniques to evaluate thermal conductivity for some soils10.1007/s00231-002-0295-x ↗
13. Experimental study on the thermal conductivity of aeolian sand from the Tibetan Plateau10.1016/j.coldregions.2017.11.006 ↗
14. Variation of the thermal conductivity of a silty clay during a freezing-thawing process10.1016/j.ijheatmasstransfer.2018.02.118 ↗
15. An improved steady-state apparatus for measuring thermal conductivity of soils10.1016/j.ijheatmasstransfer.2014.01.034 ↗
16. Sensitivity analysis of temperature changes for determining thermal properties of partially frozen soil with a dual probe heat pulse sensor10.1016/j.coldregions.2018.03.022 ↗
17. Evaluation of 11 soil thermal conductivity schemes for the permafrost region of the central Qinghai-Tibet Plateau10.1016/j.catena.2020.104608 ↗
18. Review of soil thermal conductivity and predictive models10.1016/j.ijthermalsci.2017.03.013 ↗
19. Kersten, M.S. (1949). Laboratory Research for the Determination of the Thermal Properties of Soils, University of Minnesota.
20. Johansen, O. (1975). Thermal Conductivity of Soils, Trondheim University.
21. A generalized model for calculating the thermal conductivity of freezing soils based on soil components and frost heave10.1016/j.ijheatmasstransfer.2019.119166 ↗
22. Estimating thermal conductivity of frozen soils from air-filled porosity10.1002/saj2.20102 ↗
23. A modified normalized model for predicting effective soil thermal conductivity10.1007/s11440-017-0563-z ↗
24. An improved model for predicting the thermal conductivity of sand based on a grain size distribution parameter10.1016/j.ijheatmasstransfer.2023.124021 ↗
25. Modeling soil thermal conductivities over a wide range of conditions10.1139/s05-007 ↗
26. Assessment of soil thermal conduction using artificial neural network models10.1016/j.coldregions.2019.102907 ↗
27. Estimating the thermal conductivity of soils using six machine learning algorithms10.1016/j.icheatmasstransfer.2022.106139 ↗
28. Comparative analysis of seven machine learning algorithms and five empirical models to estimate soil thermal conductivity10.1016/j.agrformet.2022.109080 ↗
29. Determining the thermal conductivity of clay during the freezing process by artificial neural network10.1155/2021/5555565 ↗
30. A novel technique based on the improved firefly algorithm coupled with extreme learning machine (ELM-IFF) for predicting the thermal conductivity of soil10.1007/s00366-021-01329-3 ↗
31. Measurement of thermal conductivity for frozen soil at temperatures close to 0 °C10.1016/j.measurement.2019.03.069 ↗
32. Prediction of the thermal conductivity of freezing soils using the soil freezing characteristic curve10.1016/j.icheatmasstransfer.2023.107078 ↗
33. Characterization of thermal conductivity of seasonally frozen turfy soil from Northeastern China10.1007/s10064-022-02977-5 ↗
34. Investigation of the thermal conductivity of soil subjected to freeze-thaw cycles using the artificial neural network model10.1007/s10973-021-11081-x ↗
35. Experimental study and modelling of the thermal conductivity of frozen sandy soil at different water contents10.1016/j.measurement.2021.109586 ↗
36. A review and evaluation of 39 thermal conductivity models for frozen soils10.1016/j.geoderma.2020.114694 ↗
37. Effect of clay content on the thermal conductivity of unfrozen and frozen sandy soils10.1016/j.ijheatmasstransfer.2023.123923 ↗
38. A simplified de Vries-based model to estimate thermal conductivity of unfrozen and frozen soil10.1111/ejss.12366 ↗
39. Modeling thermal conductivity of soils during a freezing process10.1007/s00231-021-03110-0 ↗
40. A generalized model for estimating effective soil thermal conductivity based on the Kasubuchi algorithm10.1016/j.geoderma.2019.06.031 ↗
41. (2002). Code for Investigation of Geotechnical Engineering (Standard No. GB 50021-2001).
42. Study on thermal conductivity testing and prediction model of fine sandy soil in near phasetransition zone
43. Dong, J., Li, Y., and Wang, M. (2019). Fast multi-objective antenna optimization based on RBF neural network surrogate model optimized by improved PSO algorithm. Appl. Sci., 9.10.3390/app9132589 ↗
44. Huang, L., Asteris, P., Koopialipoor, M., Armaghani, D.J., and Tahir, M.M. (2019). Invasive weed optimization technique-based ANN to the prediction of rock tensile strength. Appl. Sci., 9.10.3390/app9245372 ↗
45. Andersland, O.B., and Ladanyi, B. (2004). Frozen Ground Engineering, John Wiley and Sons Inc.
46. A new thermo-time domain reflectometry approach to quantify soil ice content at temperatures near the freezing point10.1016/j.coldregions.2020.103060 ↗