To explore the cooling effect of phase change materials (PCM) on asphalt pavement, a numerical model of the coupled heat transfer process of a typical monolithic subgrade of the G7 Expressway in the eastern Tianshan mountain area was developed. Three types of paraffin materials (OP55E, OP52E, OP47E) were mixed in a 4:3:3 volume ratio and blended into the asphalt upper layer and overall asphalt layer at volume ratios of 5%, 10%, 15% and 20%. The cooling effect of different PCM addition schemes was simulated and analyzed, and the frequency and duration of asphalt pavement high temperature operation status were also measured. The results showed that: (1) Th addition of PCM in the asphalt layer can effectively reduce the frequency of pavement high temperature rutting damage. The number of days and average daily duration of high temperature on the road surface were both reduced. (2) The cooling effect was positively correlated with the PCM volume mixing ratio, and the temperature drop of the pavement also increased with the increase of the PCM blending ratio. As the PCM mixing ratio increased from 5% to 20%, the initial 75 °C pavement cooled by 1.49 °C and 4.66 °C, respectively, and the number of days and hours of pavement temperature over 70 °C decreased to 4 days and 3.3 h, respectively. (3) The cooling effect of the asphalt upper layer PCM scheme was greater at a small mixing ratio (5%), whereas the performance of the overall asphalt layer PCM blended scheme was effectively promoted by increasing the equivalent heat capacity of system under the large mixing ratio (20%).

Revealing the variation law of thermal diffusivity of sandy soil can provide a theoretical basis for the engineering design and construction in cold and arid regions. Based on experimental data of sandy soil samples, the distribution characteristics and influence of dry density and moisture content on thermal diffusivity are analyzed in this work. Then, the prediction model based on the empirical fitting formula and RBF neural network method for thermal diffusivity of frozen and unfrozen sandy soil is established, and the prediction accuracy of different prediction methods is compared. The results show that (1) thermal diffusivity of sandy soil is positively correlated with the particle size. With the increase of sand size, thermal diffusivity of sandy soil increases significantly. (2) Partial correlation among natural moisture content, dry density, and thermal diffusivity varies with different frozen and unfrozen conditions. (3) For unfrozen sandy soil, the binary RBF neural network prediction model is obviously better than that of the binary empirical fitting formula model. (4) The ternary prediction model has significantly higher prediction accuracy than that of the binary prediction model for frozen sandy soil, and the ternary RBF neural network model has the best prediction effect among the four methods.

The comprehensive understanding of the variation law of soil thermal conductivity is the prerequisite of design and construction of engineering applications in permafrost regions. Compared with the unfrozen soil, the specimen preparation and experimental procedures of frozen soil thermal conductivity testing are more complex and challengeable. In this work, considering for essentially multiphase and porous structural characteristic information reflection of unfrozen soil thermal conductivity, prediction models of frozen soil thermal conductivity using nonlinear regression and Support Vector Regression (SVR) methods have been developed. Thermal conductivity of multiple types of soil samples which are sampled from the Qinghai-Tibet Engineering Corridor (QTEC) are tested by the transient plane source (TPS) method. Correlations of thermal conductivity between unfrozen and frozen soil has been analyzed and recognized. Based on the measurement data of unfrozen soil thermal conductivity, the prediction models of frozen soil thermal conductivity for 7 typical soils in the QTEC are proposed. To further facilitate engineering applications, the prediction models of two soil categories (coarse and fine-grained soil) have also been proposed. The results demonstrate that, compared with nonideal prediction accuracy of using water content and dry density as the fitting parameter, the ternary fitting model has a higher thermal conductivity prediction accuracy for 7 types of frozen soils (more than 98% of the soil specimens’ relative error are within 20%). The SVR model can further improve the frozen soil thermal conductivity prediction accuracy and more than 98% of the soil specimens’ relative error are within 15%. For coarse and fine-grained soil categories, the above two models still have reliable prediction accuracy and determine coefficient (R2) ranges from 0.8 to 0.91, which validates the applicability for small sample soils. This study provides feasible prediction models for frozen soil thermal conductivity and guidelines of the thermal design and freeze-thaw damage prevention for engineering structures in cold regions.

The Qinghai–Tibet Plateau is the highest and largest permafrost area in the middle and low latitudes of China. In this region, permafrost thaw settlement is the main form of expressway subgrade disaster. Therefore, the quantitative analysis and regionalization study of permafrost thaw settlement deformation are of great significance for expressway construction and maintenance in the Qinghai–Tibet region. This paper establishes a thaw settlement prediction model using the thaw settlement coefficient and thaw depth. The thaw depth was predicted by the mean annual ground temperatures and active-layer thicknesses using the Radial Basis Function (RBF) neural network model, and the thaw settlement coefficient was determined according to the type of ice content. Further, the distribution characteristics of thaw settlement risk of the permafrost subgrade in the study region were mapped and analyzed. The results showed that the thaw settlement risk was able to be divided into four risk levels, namely significant risk, high risk, medium risk and low risk levels, with the areas of these four risk levels covering 3868.67 km2, 1594.21 km2, 2456.10 km2 and 558.78 km2, respectively, of the total study region. The significant risk level had the highest proportion among all the risk levels and was mainly distributed across the Chumar River Basin, Beiluhe River Basin and Gaerqu River Basin regions. Moreover, ice content was found to be the main factor affecting thaw settlement, with thaw settlement found to increase as the ice content increased.

The aim of this paper was to reveal the distribution law of permafrost thermal thawing sensibility and thaw depth caused by road construction in Qinghai-Tibet engineering corridor (QTEC). The prediction models of permafrost thermal thawing sensibility and thaw depth have been developed by incorporating the MODIS and in situ soil temperature observation data. The comprehensive earth-atmosphere-coupled numerical models of different embankment structures have been utilized to calculate the thaw depth of the underlying permafrost foundation. Finally, using the given data and above developed prediction models, the distribution maps of permafrost thermal thawing sensibility and thaw depth in QTEC are obtained by grid calculation. The results show the following: (1) Insensitive permafrost of QTEC mainly distributes in the large-scale mountain and high latitude area, and highly sensitive permafrost is located in the perennial river bed, flood plain, and terrace regions. (2) Road construction has a strong thermal disturbance to underlying permafrost, and the proportion of large thaw depth area of separate embankment is obviously smaller than that of 26 m full-width embankment. (3) Increase of subgrade interval reduces the proportion of large thaw depth areas, and the application of separate embankment structure is an effective engineering means for the Qinghai-Tibet expressway.