Determining the Thermal Conductivity of Clay during the Freezing Process by Artificial Neural Network

Xiuling Ren,Yanhui You,Pan Yue,Qihao Yu,Guike Zhang,Mingyang Jin,Xiangtian Xu

doi:10.1155/2021/5555565

Abstract

Thermal conductivity is an important thermal parameter in engineering design in cold regions. By measuring the thermal conductivity of clay using a transient hot-wire method in the laboratory, the influential factors of the thermal conductivity of soils during the freezing process were analyzed, and a predictive model of thermal conductivity was developed with an artificial neural network (ANN) technology. The results show that the variation of thermal conductivity can be divided into three stages with decreasing temperature, positive temperature stage, transition stage, and negative temperature stage. The thermal conductivity increases sharply in the transition stage. The difference between the thermal conductivity at positive and negative temperature is small when the dry density of the soil specimens is larger than the critical dry density, while the difference is large if the dry density is less than the critical dry density. As the negative temperature decreases, the larger the moisture content of the soil specimens, the larger the increase of thermal conductivity. The effect of initial moisture content on thermal conductivity is more significant than that of dry density and temperature. The change tendency of the thermal conductivity calculated by the established ANN model is basically consistent with that of the laboratory-measured values, indicating that this model can be able to accurately predict the thermal conductivity of the soil specimens in the freezing process.

Highlights

Academic Editor: Xiangtian Xu ermal conductivity is an important thermal parameter in engineering design in cold regions
E results show that the variation of thermal conductivity can be divided into three stages with decreasing temperature, positive temperature stage, transition stage, and negative temperature stage. e thermal conductivity increases sharply in the transition stage. e difference between the thermal conductivity at positive and negative temperature is small when the dry density of the soil specimens is larger than the critical dry density, while the difference is large if the dry density is less than the critical dry density
As the negative temperature decreases, the larger the moisture content of the soil specimens, the larger the increase of thermal conductivity. e effect of initial moisture content on thermal conductivity is more significant than that of dry density and temperature. e change tendency of the thermal conductivity calculated by the established artificial neural network (ANN) model is basically consistent with that of the laboratory-measured values, indicating that this model can be able to accurately predict the thermal conductivity of the soil specimens in the freezing process

Summary

Introduction

Academic Editor: Xiangtian Xu ermal conductivity is an important thermal parameter in engineering design in cold regions. As the negative temperature decreases, the larger the moisture content of the soil specimens, the larger the increase of thermal conductivity. E thermal conductivity of soils is mainly affected by mineral types, moisture content, dry density, temperature, and grain size [2,3,4,5,6,7,8,9,10,11]. In terms of empirical models, Kersten [4] used a single thermal probe method to test the thermal conductivity of 19 different types of soils by considering the effect of moisture content and dry bulk density and established the empirical equations of the thermal conductivity for silt and clay soils and sandy soils in the frozen and unfrozen state, respectively. Bi et al [24] proposed a generalized model for the thermal conductivity of the freezing soils based on frost heave and components of soils

Objectives

Methods

Results