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Acta Geotechnica

, Volume 14, Issue 6, pp 2007–2029 | Cite as

Improving a thermal conductivity model of unsaturated soils based on multivariate distribution analysis

  • Haifeng Zou
  • Nan ZhangEmail author
  • Anand J. Puppala
Research Paper
  • 124 Downloads

Abstract

Soil thermal conductivity (k) is a key parameter for the design of energy geo-structures, and it depends on many soil properties such as saturation degree, porosity, mineralogical composition, soil type and others. Capturing these diversified influencing factors in a soil thermal conductivity model is a challenging task for engineers due to the nonlinear dependencies. In this study, a multivariate distribution approach was utilized to improve an existing soil thermal conductivity model, Cote and Konrad model, by quantitatively considering the impacts of dry density (ρd), porosity (n), saturation degree (Sr), quartz content (mq), sand content (ms) and clay content (mc) on thermal conductivity of unsaturated soils. A large database containing these seven soil parameters was compiled from the literature to support the multivariate analysis. Simplified bivariate and multivariate correlations for improving the Cote and Konrad model were derived analytically and numerically to consider different influencing factors. By incorporating these simplified correlations, the predicted k values were more concentrated around the measured values with the coefficient of determination (R2) increased from 0.83 to 0.95. It is concluded that the developed correlations with the information of different soil properties provide an efficient, rational and simple way to predict soil thermal conductivity more accurately. Moreover, the quartz content is a more important factor than the porosity that shall be considered in the establishment of thermal conductivity models for unsaturated soils with high quartz content.

Keywords

Multivariate correlations Numerical simulation Quartz content Soil thermal conductivity Unsaturated soils 

Notes

Acknowledgements

Majority of the work presented in this paper was funded by the National Key R&D Program of China (2016YFC0800200) and the National Natural Science Foundation of China (Grant No. 41672294). These financial supports are gratefully acknowledged. The authors would like to acknowledge the researchers who published the data in the literature but findings and conclusions expressed in this paper do not reflect the views of those researchers.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Civil and Environmental EngineeringThe Hong Kong University of Science and TechnologyClear Water BayChina
  2. 2.Institute of Geotechnical EngineeringSoutheast UniversityNanjingChina
  3. 3.Department of Civil EngineeringThe University of Texas at ArlingtonArlingtonUSA

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