As an important thermal parameter in engineering design in cold regions, thermal conductivity determines the frozen and thawed depth of soils, which will affect the heat transfer process of soils.

Artificial neural networks (ANNs) are also widely used in the prediction of thermal parameters. Some researchers used the ANN models to calculate the thermal conductivity and thermal diffusivity of soils. However, the ANN technology is seldom used to establish the predictive models of the thermal conductivity of soils during a freezing process.

In a study published in Advances in Materials Science and Engineering, researchers from the Northwest Institute of Eco-Environment and Resources (NIEER) of the Chinese Academy of Sciences (CAS) analyzed the influential factors of the thermal conductivity of soils during the freezing process and developed a predictive model of thermal conductivity with an ANN technology.

The researchers measured the thermal conductivity of clay under the influence of initial moisture content, initial dry density, and temperature, and then they analyzed the variation of the thermal conductivity of the soil specimens during a freezing process where the temperature changes from positive to negative.

Besides, the researchers also developed a predictive model via the ANN technology to calculate the thermal conductivity of the soil specimens under the same experimental conditions.

The results showed that the variation of thermal conductivity could be divided into three stages with decreasing temperature, positive temperature stage, transition stage, and negative temperature stage. The thermal conductivity increased sharply in the transition stage.

The study results will help to understand the variation of the thermal conductivity of clay under various impacting factors during a freezing process and to develop a predictive model of the thermal conductivity of soil during the freezing process by ANN technology.