Thermal properties of sand are of importance in numerous engineering and scientific applications ranging from energy storage and transportation infrastructures to underground construction. All these applications require knowledge of the effective thermal parameters for proper operation. The traditional approaches for determination of the effective thermal property, such as the thermal conductivity are based on very costly, tedious and time-consuming experiments. The recent developments in computer science have allowed the use of soft and hard computational methods to compute the effective thermal conductivity (ETC). Here, two computation methods are presented based on soft and hard computing approaches, namely, the deep neural network (DNN) and the thermal lattice element method (TLEM), respectively, to compute the ETC of sands with varying porosity and moisture content values. The developed models are verified and validated with a small data set reported in the literature. The computation results are compared with the experiments, and the numerical results are found to be within reasonable error bounds. The deep learning method offers fast and robust implementation and computation, even with a small data set due to its superior backpropagation algorithm. However, the TLEM based on micro and meso physical laws outperforms it at accuracy.

沙的热学性质在从储能和运输基础设施到地下建筑的众多工程和科学应用中都很重要。所有这些应用都需要了解有效的热参数才能正常运行。确定有效热特性（例如导热系数）的传统方法是基于非常昂贵，繁琐且耗时的实验。计算机科学的最新发展已允许使用软和硬计算方法来计算有效导热率（ETC）。这里，基于软和硬计算方法，提出了两种计算方法，分别是深度神经网络（DNN）和热晶元方法（TLEM），计算具有不同孔隙率和水分含量值的沙子的ETC。使用文献中报告的少量数据对开发的模型进行验证和确认。将计算结果与实验结果进行比较，发现数值结果在合理的误差范围内。深度学习方法由于其出色的反向传播算法，即使数据集很小，也可以提供快速，强大的实现和计算能力。但是，基于微观和中观物理定律的TLEM在准确性上优于其。深度学习方法由于其出色的反向传播算法，即使数据集很小，也可以提供快速，强大的实现和计算能力。但是，基于微观和中观物理定律的TLEM在准确性上优于其。深度学习方法由于其出色的反向传播算法，即使数据集很小，也可以提供快速，强大的实现和计算能力。但是，基于微观和中观物理定律的TLEM在准确性上优于其。