Machine-Learning-Based Parameterisation of Soil Thermal Conductivity for Shallow Geothermal and Ground Heat Exchanger Modelling

Mateusz Żeruń; Ewa Jagoda; Edyta Majer

doi:10.3390/en19081827

journal article Open Access Apr 08, 2026

Machine-Learning-Based Parameterisation of Soil Thermal Conductivity for Shallow Geothermal and Ground Heat Exchanger Modelling

Mateusz Żeruń

Ewa Jagoda

Edyta Majer

Energies Vol. 19 No. 8 pp. 1827 · MDPI AG

View at Publisher Save 10.3390/en19081827

Abstract

Thermal conductivity is a key input parameter in geotechnical and shallow geothermal engineering, directly influencing the design, efficiency, and long-term performance of ground heat exchangers, energy piles, and ground-source heat pump systems. Reliable parameterisation of this property in sandy soils remains challenging due to nonlinear interactions between water content, bulk density, and soil structure. This study develops a machine-learning-based workflow for robust parameterisation of thermal conductivity in quartz-rich sands using a large, internally consistent laboratory dataset comprising 1716 samples, including 1455 moist measurements used for modelling, obtained from nationwide site investigations. Air-dry specimens were identified as laboratory-induced drying states and excluded to restrict the analysis to hydro-mechanical conditions representative of typical shallow subsurface environments. Several regression algorithms representing different modelling strategies were evaluated within a unified and reproducible framework and benchmarked against selected classical empirical formulations. Model performance was assessed using standard accuracy metrics together with diagnostics describing the functional stability of predicted thermal-conductivity surfaces. The results reveal a systematic trade-off between predictive accuracy and functional consistency, indicating that models optimised for accuracy may produce functionally unstable and less suitable parameterisations for engineering applications. Accuracy-optimised models frequently produce locally irregular parameter fields, whereas more strongly regularised models yield smoother and physically more coherent response surfaces. The proposed workflow supports reliable thermal-property parameterisation for geotechnical design and shallow geothermal modelling.

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Details

Published: Apr 08, 2026
Vol/Issue: 19(8)
Pages: 1827
License: View

Authors

M

Mateusz Żeruń

Department of Engineering Geology and Shallow Geothermal Energy, Polish Geological Institute—National Research Institute, 76 Jagiellońska St., 03-301 Warsaw, Poland

E

Ewa Jagoda

Department of Engineering Geology and Shallow Geothermal Energy, Polish Geological Institute—National Research Institute, 76 Jagiellońska St., 03-301 Warsaw, Poland

E

Edyta Majer

Department of Engineering Geology and Shallow Geothermal Energy, Polish Geological Institute—National Research Institute, 76 Jagiellońska St., 03-301 Warsaw, Poland

Cite This Article

Mateusz Żeruń, Ewa Jagoda, Edyta Majer (2026). Machine-Learning-Based Parameterisation of Soil Thermal Conductivity for Shallow Geothermal and Ground Heat Exchanger Modelling. Energies, 19(8), 1827. https://doi.org/10.3390/en19081827

Machine-Learning-Based Parameterisation of Soil Thermal Conductivity for Shallow Geothermal and Ground Heat Exchanger Modelling

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