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Theoretical calculation model for the thermal conductivity of scrap tire rubber–sand mixtures based on soil components

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Abstract

Scrap tire rubber is light mass and has heat-insulation properties. It can be mixed with building materials to form a new type of sustainable composite. Thermal conductivity is an important parameter affecting the thermal properties of building materials. Herein, the thermal conductivity of scrap rubber–sand mixtures was measured using a thermal probe; the effects of rubber content, moisture content, and dry density on the thermal conductivity of the mixture were studied, and a theoretical calculation model for the thermal conductivity of scrap rubber–sand mixtures was established. The results show that the thermal conductivity of scrap rubber–sand mixtures decreases with increases in the rubber content and reductions in the dry density. The thermal conductivity of the mixture increases with increases in moisture content. The established theoretical calculation model can accurately calculate the thermal conductivity of scrap rubber–sand mixtures, with R2 = 0.88, MAE = 0.044 (Wm−1 K−1), and RMSE = 0.071 (Wm−1 K−1). The prediction accuracy of the theoretical model is obviously superior to that of the traditional empirical model. It provides an accurate method for predicting the thermal conductivity of scrap rubber–sand mixtures.

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References

  1. Yadav JS, Tiwari SK. Effect of waste rubber fibres on the geotechnical properties of clay stabilized with cement. Appl Clay Sci. 2017;149:97–110.

    Article  CAS  Google Scholar 

  2. Arulrajah A, Mohammadinia A, Maghool F, Horpibulsuk S. Tyre derived aggregates and waste rock blends: resilient moduli characteristics. Constr Build Mater. 2019;201:207–17.

    Article  Google Scholar 

  3. Nezhad MG, Tabarsa A, Latifi N. Effect of natural and synthetic fibers reinforcement on California bearing ratio and tensile strength of clay. J Rock Mech Geotech Eng. 2021;13:626–42.

    Article  Google Scholar 

  4. Neaz Sheikh M, Mashiri MS, Vinod JS, Tsang H-H. Shear and compressibility behavior of sand–tire crumb mixtures. J Mater Civ Eng. 2013;25:1366–74.

    Article  Google Scholar 

  5. Tafreshi SNM, Mehrjardi GhT, Dawson AR. Buried pipes in rubber–soil backfilled trenches under cyclic loading. J Geotech Geoenviron Eng. 2012;138:1346–56.

    Article  Google Scholar 

  6. Mounanga P, Gbongbon W, Poullain P, Turcry P. Proportioning and characterization of lightweight concrete mixtures made with rigid polyurethane foam wastes. Cement Concr Compos. 2008;30:806–14.

    Article  CAS  Google Scholar 

  7. Hennebert P, Lambert S, Fouillen F, Charrasse B. Assessing the environmental impact of shredded tires as embankment fill material. Can Geotech J. 2014;51:469–78.

    Article  Google Scholar 

  8. Zornberg JG, Cabral AR, Viratjandr C. Behaviour of tire shred sand mixtures. Can Geotech J. 2004;41:227–41.

    Article  Google Scholar 

  9. Chaney R, Demars K, Feng Z-Y, Sutter K. Dynamic properties of granulated rubber/sand mixtures. Geotech Test J. 2000;23:338.

    Article  Google Scholar 

  10. Anastasiadis A, Senetakis K, Pitilakis K, Gargala C, Karakasi I, Edil T, et al. Dynamic behavior of sand/rubber mixtures. Part I: effect of rubber content and duration of confinement on small-strain shear modulus and damping ratio. J ASTM Int. 2012;9:103680.

    Article  Google Scholar 

  11. Hazarika H, Kohama E, Sugano T. Underwater shake table tests on waterfront structures protected with tire chips cushion. J Geotech Geoenviron Eng. 2008;134:1706–19.

    Article  Google Scholar 

  12. Orakoglu Firat ME, Atila O. Investigation of the thermal conductivity of soil subjected to freeze–thaw cycles using the artificial neural network model. J Therm Anal Calorim. 2022;147:8077–93.

    Article  CAS  Google Scholar 

  13. Wang C, Cai G, Wu M, Zhao Z. Prediction of soil thermal conductivity based on multivariate probability distribution models. Int Commun Heat Mass Transfer. 2022;138: 106355.

    Article  Google Scholar 

  14. Wang W, Chen C, Xu W, Li C, Li Y-Z. Experimental research on heat transfer characteristics and temperature rise law of in situ thermal remediation of soil. J Therm Anal Calorim. 2022;147:3365–78.

    Article  CAS  Google Scholar 

  15. Liang B, Chen M, Guan J. Assessment on the thermal and moisture migration of sand-based materials coupled with kaolin additive. J Therm Anal Calorim. 2022;147:10163–76.

    Article  CAS  Google Scholar 

  16. Li CY, Liu HB, Wei HB. An experimental study on engineering properties of rubber particles-improved fly ash soil. AMM. 2011;71–78:3401–6.

    Article  Google Scholar 

  17. Lee JK, Shang JQ. Thermal properties of mine tailings and tire crumbs mixtures. Constr Build Mater. 2013;48:636–46.

    Article  Google Scholar 

  18. Lee JK, Shang JQ, Jeong S. Thermal conductivity of compacted fill with mine tailings and recycled tire particles. Soils Found. 2015;55:1454–65.

    Article  Google Scholar 

  19. Liu L, Cai G, Liu X. Investigation of thermal conductivity and prediction model of recycled tire rubber–sand mixtures as lightweight backfill. Constr Build Mater. 2020;248: 118657.

    Article  Google Scholar 

  20. Xiao Y, Nan B, McCartney JS. Thermal conductivity of sand–tire shred mixtures. J Geotech Geoenviron Eng. 2019;145:06019012.

    Article  CAS  Google Scholar 

  21. Zhang T, Cai G, Liu S, Duan W. Laboratory observation of engineering properties and deformation mechanisms of cemented rubber–sand mixtures. Constr Build Mater. 2016;120:514–23.

    Article  CAS  Google Scholar 

  22. Selig E, Ladd R. Preparing test specimens using undercompaction. Geotech Test J. 1978;1:16.

    Article  Google Scholar 

  23. Zhang T, Cai G, Liu S, Puppala AJ. Investigation on thermal characteristics and prediction models of soils. Int J Heat Mass Transf. 2017;106:1074–86.

    Article  Google Scholar 

  24. Cai G, Zhang T, Puppala AJ, Liu S. Thermal characterization and prediction model of typical soils in Nanjing area of China. Eng Geol. 2015;191:23–30.

    Article  Google Scholar 

  25. Zhang N, Wang Z. Review of soil thermal conductivity and predictive models. Int J Therm Sci. 2017;117:172–83.

    Article  Google Scholar 

  26. Horai K. Thermal conductivity of rock-forming minerals. J Geophys Res. 1971;76:1278–308.

    Article  CAS  Google Scholar 

  27. Stephan K, Laesecke A. The Thermal Conductivity of Fluid Air. J Phys Chem Ref Data. 1985;14:227–34.

    Article  CAS  Google Scholar 

  28. Salomone LA, Kovacs WD. Thermal resistivity of soils. J Geotech Eng. 1984;110:375–89.

    Article  Google Scholar 

  29. Bi J, Zhang M, Lai Y, Pei W, Lu J, You Z, et al. A generalized model for calculating the thermal conductivity of freezing soils based on soil components and frost heave. Int J Heat Mass Transf. 2020;150: 119166.

    Article  Google Scholar 

  30. Bi J, Zhang M, Chen W, Lu J, Lai Y. A new model to determine the thermal conductivity of fine-grained soils. Int J Heat Mass Transf. 2018;123:407–17.

    Article  Google Scholar 

  31. Tong F, Jing L, Zimmerman RW. An effective thermal conductivity model of geological porous media for coupled thermo-hydro-mechanical systems with multiphase flow. Int J Rock Mech Min Sci. 2009;46:1358–69.

    Article  Google Scholar 

  32. Kannuluik W, Carman E. The temperature dependence of the thermal conductivity of air. Aust J Chem. 1951;4:305.

    Article  Google Scholar 

  33. Tang A-M, Cui Y-J, Le T-T. A study on the thermal conductivity of compacted bentonites. Appl Clay Sci. 2008;41:181–9.

    Article  CAS  Google Scholar 

  34. Chen Y, Zhou S, Hu R, Zhou C. Estimating effective thermal conductivity of unsaturated bentonites with consideration of coupled thermo-hydro-mechanical effects. Int J Heat Mass Transf. 2014;72:656–67.

    Article  Google Scholar 

  35. Wang C, Cai G, Liu X, Wu M. Prediction of soil thermal conductivity based on Intelligent computing model. Heat Mass Transfer. 2022;58:1695–708.

    Article  Google Scholar 

  36. Gokceoglu C. A fuzzy triangular chart to predict the uniaxial compressive strength of the Ankara agglomerates from their petrographic composition. Eng Geol. 2002;66:39–51.

    Article  Google Scholar 

  37. Yukselen Y, Erzin Y. Artificial neural networks approach for zeta potential of Montmorillonite in the presence of different cations. Environ Geol. 2008;54:1059–66.

    Article  CAS  Google Scholar 

  38. Kersten M. Laboratory research for the determination of the thermal properties of soil. ACFEL Tech Rep. 1949;23.

  39. Gangadhara Rao M, Singh DN. A generalized relationship to estimate thermal resistivity of soils. Can Geotech J. 1999;36:767–73.

    Article  Google Scholar 

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Acknowledgements

This paper was supported by National Science Fund for Distinguished Young Scholars (Grant No. 42225206), the National Natural Science Foundation of China (Grant No. 41877231, No. 42072299), and Project of Jiangsu Province Transportation Engineering Construction Bureau (7921004042B).

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Contributions

CW: Conceptualization; Methodology; Investigation; Formal analysis; Data Curation; Visualization; Writing. MW: Conceptualization; Visualization; Writing; Supervision. GC: Formal Analysis; Investigation; Data Curation; Visualization; Writing. JC: Conceptualization; Visualization; Writing. ZZ: Investigation; Visualization; Writing.

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Correspondence to Guojun Cai.

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Wang, C., Wu, M., Cai, G. et al. Theoretical calculation model for the thermal conductivity of scrap tire rubber–sand mixtures based on soil components. J Therm Anal Calorim 148, 11041–11051 (2023). https://doi.org/10.1007/s10973-023-12329-4

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  • DOI: https://doi.org/10.1007/s10973-023-12329-4

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