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.

Quantitative studies of the heat transfer mechanism of permafrost subgrades and its effect on the permafrost under the subgrade are crucial for the study of permafrost subgrade disposal measures; however, few studies have been conducted in this area. In the present work, by quantitatively analyzing the permafrost subgrade heat transfer mechanism and the variations in the underlying permafrost, the preliminary parameters of the interface heat control method—such as the application period, position, and imported cold energy quantity—are determined. The cooling effects of the ideal interface heat control method for different application schemes are analyzed. Finally, by determining the optimized temporal inhomogeneous interface energy control strategy, the required inlet velocity and artificial permafrost table for a mechanical ventilation permafrost subgrade are calculated and compared. The results show that (1) the suitable cold energy application position and period are a 0.5 m interface above the subgrade bottom and the lower thaw season, respectively, and that the imported cold energy needs to vary within the subgrade service life; (2) by adopting interface heat control measures, the maximum difference between the artificial permafrost table under the subgrade and the nearby natural ground table is only 0.097 m, and the temperature of the underlying permafrost and the area of the thawing bowl are significantly reduced; and (3) the mechanical ventilation subgrade employing the cold energy importing strategy of the interface heat control parameter also achieves a protection effect for permafrost, but as the cold air inside the ventilation pipe is gradually heated, it is necessary to amplify the inlet air speed to a certain extent for a better cooling effect.

As a result of global warming, the thawing settlement disasters of permafrost in the Qinghai–Tibet Engineering Corridor (QTEC) have intensified, which has serious effects on the safe operation of permafrost highway engineering. In this work, a prediction model for the thawing depth of permafrost subgrade in the QTEC under the climate warming scenario was established. Based on the survey results of permafrost ice content along the QTEC and the classification of thawing settlement risks, the zoning characteristics of thawing settlement of permafrost subgrade in the QTEC were obtained and analyzed. The results showed that the thawing depth of permafrost underlying the 26 m width subgrade in the QTEC will mainly remain below 9 m, and the area with a thawing depth of 6~9 m will have the widest spread within the next 20 years. The thawing settlement will be between 0.02 m and 5.45 m, with an average value of about 0.93 m after 20 years. Furthermore, after 50 years, the thawing depth of permafrost underlying the 26 m width subgrade will almost always be greater than 9 m, and the average thawing settlement will be about 1.12 m. Within the next 20 to 50 years, the risk of permafrost subgrade thawing settlement in the QTEC will be the most significant risk type, and this effect will mainly be distributed in the Kunlun Mountains, Chumar River Plain, Kekexili Mountains, Beiluhe Basin, Tanggula Mountains and intermountain Basins.

Due to increasing human engineering activities in cold regions, the precise measurement of frozen soil’s physical property parameters has become particularly important. Traditional measurements of thermal conductivity and unfrozen water content of frozen soil are usually tested separately, leading to errors in accurately understanding the dynamic variation law of permafrost’s hydrothermal parameters in the near-phase transition zone. To address this, a multi-sensor fusion technology–thermo time domain reflectometry (thermo-TDR) sensor was designed and optimized for measuring the unfrozen water content and thermal conductivity of frozen soil. Three-dimensional thermal and electromagnetic numerical models were developed to analyze and validate the design parameters of the proposed sensor. Furthermore, a corresponding validation experiment was carried out to confirm the usability and accuracy of the designed sensor. The results show that (1) under the optimized probe parameters, the deviation between the theoretical thermal conductivity and the numerical preset value is 2.94%, verifying the accuracy of the sensor in thermal physical testing. (2) With a 10 mm probe spacing design, the test area of the thermo-TDR significantly increased, and the skin effect coefficient reached 25.54%, satisfying the electromagnetic design requirements of the TDR method. (3) The designed thermo-TDR sensor realizes the simultaneous measurement of unfrozen water and thermal conductivity of frozen soil, and the experimental results present a good consistency with that of the nuclear magnetic resonance (NMR) and transient planar heat source methods. (4) Additionally, due to the drastic changes in the soil’s physical properties due to the probe’s heating process, testing errors of the thermo-TDR sensor will significantly increase in the near-phase transition range, especially in the range of −2~−1 °C.

The sunny–shady slope effect (SSSE) disrupts the thermal balance of permafrost subgrades, resulting in asymmetric thaw plates that lead to structural deformations such as longitudinal cracking and slope instability along the Qinghai–Tibet Highway (QTH). This study proposes three morphological indicators—road shoulder thawing depth difference (RSTDD), offset distance (OD), and active layer thickness difference (ALTD)—to quantitatively characterize the asymmetry of thaw plates. Through integrating remote sensing data and large-scale geological survey results with an earth–atmosphere coupled numerical model and a random forest (RF) prediction framework, we assessed the spatial distribution of thaw asymmetry along the permafrost section of the QTH. The results indicate the following: (1) The ALTD values are overall very small and almost unaffected by the SSSE. The RSTDD increases with mean annual ground temperature (MAGT) before stabilizing, while the OD shows no significant response to the MAGT. The RSTDD and OD ranges are 0–3.38 m and 0–8.65 m, respectively, and they are greatly affected by the SSSE. (2) The RSTDD and OD show obvious spatial differences in different geographical regions of the QTH. An RSTDD greater than 2 m is concentrated in the Xidatan Faulted Basin and Chumar River High Plain. An OD greater than 3 m is mainly distributed from the Chumar River High Plain to the Tongtian River Basin. (4) The RSTDD and OD are most affected by subgrade orientation with importance values of 49.84% and 51.80%, respectively. The importance of the effect of mean average ground temperature (MAGT) on the active layer thickness is 80.58%.

Soil thermal conductivity in the near-phase-transition zone is a key parameter affecting the thermal stability of permafrost engineering and its catastrophic thermal processes. Therefore, accurately determining the soil thermal conductivity in this specific temperature zone has important theoretical and engineering significance. In the present work, a method for testing the thermal conductivity of fine sandy soil in the near-phase-transition zone was proposed by measuring thermal conductivity with the transient plane heat source method and determining the volumetric specific heat capacity by weighing unfrozen water contents. The unfrozen water content of sand specimens in the near-phase-transition zone was tested, and a corresponding empirical fitting formula was established. Finally, based on the testing results, temperature variation trends and parameter influence laws of thermal conductivity in the near-phase-transition zone were analyzed, and thermal conductivity prediction models based on multiple regression (MR) and a radial basis function neural network (RBFNN) were also established. The results show the following: (1) The average error of the proposed test method in this work and the reference steady-state heat flow method is only 7.25%, which validates the reliability of the proposed test method. (2) The variation in unfrozen water contents in fine sandy soil in the range of 0~−3 °C accounts for over 80% of the variation in the entire negative temperature range. The unfrozen water content and thermal conductivity curves exhibit a similar trend, and the near-phase-transition zone can be divided into a drastic phase transition zone and a stable phase transition zone. (3) Increases in the thermal conductivity of fine sandy soil mainly occur the drastic phase transition zone, where these increases account for about 60% of the total increase in thermal conductivity in the entire negative temperature region. With the increase in density and total water content, the rate of increase in thermal conductivity in the drastic phase transition zone gradually decreases. (4) The R², MAE, and RSME of the RBFNN model in the drastic phase transition zone are 0.991, 0.011, and 0.021, respectively, which are better than those of the MR prediction model.