The thermal conductivity of soils is an important parameter in environment, earth science and engineering applications. A new thermal conductivity model based on the Fredlund and Xing model was proposed to mimic the thermal conductivity of fine‐textured soils from dryness to full saturation. However, the use of a measured thermal conductivity value at a certain saturation to calculate the parameter of the thermal conductivity model reduces the ease of use. Therefore, the objective of this study was to use multiple linear regression (MLR) and artificial neural networks (ANN) to improve the thermal conductivity model. The performances of the MLR‐based model and ANN‐based model were evaluated and compared with measured data and three existing empirical models at different saturation ranges. The results showed that the MLR‐based model and ANN‐based model performed best at low saturations, the ANN‐based model performed best at saturations between 0.01 and 0.6, and the Johansen model showed the best fit to the measured data at saturations of above 0.6. In general, among the models, the ANN‐based model performed best from dryness to full saturation (root mean square error (RMSE) = 0.098 Wm⁻¹ K⁻¹ and average absolute deviation (AAD) = 0.071 Wm⁻¹ K⁻¹), followed by the MLR‐based model (RMSE = 0.110 Wm⁻¹ K⁻¹ and AAD = 0.081 Wm⁻¹ K⁻¹). The MLR‐based model and ANN‐based model are promising for the accurate calculation of the thermal conductivity of fine‐textured soils from dryness to full saturation and can be incorporated into the numerical modelling of heat and mass transfers.

中文翻译：

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基于多元线性回归和人工神经网络的细结构土壤热导率计算

土壤的热导率是环境，地球科学和工程应用中的重要参数。提出了一种基于Fredlund and Xing模型的新型导热系数模型，以模拟细密土壤从干燥到完全饱和的导热系数。但是，使用一定饱和度下的测量导热系数值来计算导热系数模型的参数会降低使用的便利性。因此，本研究的目的是使用多元线性回归（MLR）和人工神经网络（ANN）来改善导热系数模型。对基于MLR的模型和基于ANN的模型的性能进行了评估，并与测量数据和三个现有的经验模型在不同饱和度范围内进行了比较。结果表明，基于MLR的模型和基于ANN的模型在低饱和度下表现最佳，基于ANN的模型在0.01至0.6的饱和度下表现最佳，而Johansen模型在以上饱和度下表现出与测量数据的最佳拟合0.6。通常，在这些模型中，基于ANN的模型从干燥到完全饱和（根均方根误差（RMSE）= 0.098 Wm^-1 K ^-1和平均绝对偏差（AAD）= 0.071 Wm ^-1 K ^-1），然后是基于MLR的模型（RMSE = 0.110 Wm ^-1 K ^-1和AAD = 0.081 Wm ^-1 K ^-1） 。基于MLR的模型和基于ANN的模型有望为从干燥到完全饱和的精细结构化土壤的导热系数的精确计算提供基础，并可将其纳入传热和传质的数值模型中。