热导率
土壤水分
淤泥
含水量
土壤科学
材料科学
多孔性
Pedotransfer函数
导水率
人工神经网络
热的
相关系数
地温梯度
矿物学
环境科学
岩土工程
复合材料
数学
地质学
热力学
机器学习
计算机科学
统计
物理
古生物学
地球物理学
作者
Chuanyong Zhu,Zhimin He,Mingliang Du,Liang Gong,Xinyu Wang
出处
期刊:Nanotechnology
[IOP Publishing]
日期:2021-11-19
卷期号:33 (6): 065408-065408
被引量:6
标识
DOI:10.1088/1361-6528/ac3688
摘要
The effective thermal conductivity of soils is a crucial parameter for many applications such as geothermal engineering, environmental science, and agriculture and engineering. However, it is pretty challenging to accurately determine it due to soils' complex structure and components. In the present study, the influences of different parameters, including silt content (msi), sand content (msa), clay content (mcl), quartz content (mqu), porosity, and water content on the effective thermal conductivity of soils, were firstly analyzed by the Pearson correlation coefficient. Then different artificial neural network (ANN) models were developed based on the 465 groups of thermal conductivity of unfrozen soils collected from the literature to predict the effective thermal conductivity of soils. Results reveal that the parameters ofmsi,msa,mcl, andmquhave a relatively slight influence on the effective thermal conductivity of soils compared to the water content and porosity. Although the ANN model with six parameters has the highest accuracy, the ANN model with two input parameters (porosity and water content) could predict the effective thermal conductivity well with acceptable accuracy andR2=0.940. Finally, a correlation of the effective thermal conductivity for different soils was proposed based on the large number of results predicted by the two input parameters ANN-based model. This correlation has proved to have a higher accuracy without assumptions and uncertain parameters when compared to several commonly used existing models.
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