Study On Thermal Conductivity Property Of Seasonal Frozen Soil

In seasonal frozen region and permafrost region, the effect that the engineering construction makes to the temperature field is an important question. Thermal conductivity is an important parameter in temperature field calculation. Only to master the thermal conductivity of the rock materials can people forecast the temperature field of subgrade, side slope or underground projects correctly, then finish the freeze-thaw stability analysis and lagging design. Thermal conductivity is the parameter that can represent the capacity of heat transmission. The research in this paper centers around thermal conductivity to analyze the heat transmission.In seasonal frozen region, because of the change of the temperature in four seasons, freezing and melting will happen on the water in soil, which may bring on the non-uniform settlement. It will make damage to the architecture, transportation and underground pipeline, and will make big difficult to design, construction and maintenance. First do the thermal conductivity determination test and research the relationship between thermal conductivity and moisture, dry density and freeze-thaw cycling times. Results show that the bigger the moisture is, the bigger the thermal conductivity will be; the bigger the dry density is, the bigger the thermal conductivity will be; the more the freeze-thaw cycling times are, the less the thermal conductivity will be. Regard the 10cm×10cm soil samples and real subgrade as infinite cylinder model and semi infinite plane model, and get the model of temperature, heat flowing density and heat quantity by using the theories of heat transfer under the situation of temperature increase or decrease. When calculating the temperature field, just use theses model can it get the temperature, heat flowing density and heat quantity in subgrade, which makes it more quick and convenient. To ensure the stability of subgrade, the forecasting of thermal conductivity in the future is needed. Use the SPSS software and BP neural network to get the thermal conductivity fitting model. After comparing the accuracy and the difficulty level of the two theories, the precision of two theories are both high is founded. But the operation process of BP neural network is more flexible than the other and spends more time. So using the multiple linear regression method can get the desired results quickly and accurately. The model built by the multiple linear regression method shows that the moisture makes biggest effect on the thermal conductivity among the three factors, the dry density second, and freeze-thaw cycling times makes smallest effect respectively.