A new type of thermally controlled subgrade is proposed to mitigate persistent frost heave issues of railway subgrades in seasonally frozen regions. A dedicated ground-source heat pump system collects low-grade geothermal energy from the stable soil layer near the subgrade, converts it into high-grade thermal energy, and transfers it to the frigid subgrade for active heating and temperature control, thereby eliminating the adverse effects of frost heave. A 20-metre-long test of thermally controlled subgrade was constructed in a frost heave of the Junggar-Shenchi Railway in Shanxi Province, China. During the winter spanning 2021 and 2022, the heating temperature of the heat pump, the thermal regime of the test subgrade and the natural subgrade, the frost depth, and the track heave were measured. The results indicate that the heat pump temperature could reach a peak of 59.4 degrees C, with the average daily heating temperature during intermittent operation reaching 25.2 degrees C or higher, indicating an efficient heat source that plays a favourable role. The freezing period of the natural subgrade lasted for 141 days, while the subgrade in the test was 20 days shorter. The maximum frost depths at the track centre, shoulder, and embankment slope toe in the test were 88 cm, 75 cm, and 58 cm, respectively. These depths were 60 cm, 122 cm, and 78 cm less than those of the natural subgrade, effectively controlling the frost depth within the threshold that may cause potential structural damage. Under natural conditions, the track heave reached a peak of 9.4 mm, leading to a harmful frost heave scenario. In contrast, the track deformation in the test was less than 3 mm, which did not exceed the regular maintenance threshold. The thermally controlled subgrade proves to be an effective method for preventing and controlling persistent frost heave damage in critical locations such as low embankments, cut subgrades, turnout areas, and culvert roofs.

Freight transportation plays a crucial role in sustaining the Canadian economy. However, heavy truck transportation also puts enormous pressure on roadway networks. Spring Load Restrictions (SLR) are implemented to minimize road damage caused by heavy traffic during the thaw-weakening season, and Winter Weight Premium (WWP) is used to reduce the impact of SLR on trucking operations by allowing higher axle loads in winter. However, existing policies apply fixed dates each year for these restrictions, regardless of the actual structural capacity of the pavement. Different methods have been proposed to improve the application of SLR and WWP; however, they rely mainly on indirect indices, such as the cumulative thawing index and cumulative freezing index, which pose challenges in their calculation. This study explores the practical implementation of machine learning models for accurately determining the start and end dates of SLR and WWP. In a novel approach, machine learning models directly derive the start and end dates of SLR and WWP from frost and thaw depths in the pavement structure which are determined by pavement temperature and moisture content. In contrast to previous studies that neglected the influence of soil moisture content on determining the start and end dates of SLR and WWP, this study examines the variation in soil moisture content to evaluate the validity of existing theories. The findings reveal a high level of agreement between the machine learning model's estimations of frost and thaw depths and the measured values, with R2 values exceeding 0.91.

Rising temperatures due to climate change can significantly impact the freeze-thaw condition of airport pavements in cold regions. This case study investigates the implications of warming temperatures on the freeze-thaw penetration and frost heave of pavements in critical airports across Canada. To this end, different methods were used in the quantification process through climate change simulations considering emission scenario RCP8.5 in 20 and 40 year time horizons. The results show that climate change would have different design implications for airport pavements, depending on their location. The predictions suggest a shallower frost penetration depth, and possibly less frost heave, for the airports not underlain by permafrost, while airports over permafrost areas might experience an increase in thickness of the active layer, ranging from 41 to 57 percent, by 2061. Among the different methods used in this study, it was observed that some methods performed better in predicting the frost depth of fine soils, while others worked better in the frost depth prediction of coarse soils. The results indicate the need for more mechanistic models to provide a more realistic prediction of freeze-thaw penetration, as compared to existing empirical models.

Study region: Upper Heihe River Basin, Northwest China. Study focus: We investigated potential climate change under three Representative Concentration Pathways (RCP 2.6, 4.5, and 8.5) and their impacts on frozen ground in the upper Heihe River Basin using the ensemble climate data from eight general circulation models and the Soil and Water Assessment Tool (SWAT). New hydrological insights for the region: Air and ground freezing indices declined significantly during the baseline period (1976-2015), whereas the thawing indices increased, indicating the heat accumulation in study area. The frost depth, which refers to the potential frost depth of active layer in permafrost areas and the maximum frost depth in seasonally frozen areas, decreased significantly at the rate of 3 cm/10 yr. The SWAT-simulation and gray relational analysis revealed that soil water was controlled by precipitation and frost depth in spring and autumn. Compared to that of the baseline, the projected frost depth is projected to decline by 0.07-0.1 m during the near future (2020-2059) and 0.08-0.36 m for the far future (2060-2099). In addition, we developed a long-term warning system, which indicates that the degree of frozen ground degradation would be mild during the near future and would be severe for the far future under RCP 8.5. This study provides valuable insights into the protection of frozen-ground in the Upper Heihe River Basin.

In permafrost regions, the thaw depth strongly controls shallow subsurface hydrologic processes that in turn dominate catchment runoff. In seasonally freezing soils, the maximum expected frost depth is an important geotechnical engineering design parameter. Thus, accurately calculating the depth of soil freezing or thawing is an important challenge in cold regions engineering and hydrology. The Stefan equation is a common approach for predicting the frost or thaw depth, but this equation assumes negligible soil heat capacity and thus exaggerates the rate of freezing or thawing. The Neumann equation, which accommodates sensible heat, is an alternative implicit equation for calculating freeze-thaw penetration. This study details the development of correction factors to improve the Stefan equation by accounting for the influence of the soil heat capacity and non-zero initial temperatures. The correction factors are first derived analytically via comparison to the Neumann solution, but the resultant equations are complex and implicit. Explicit equations are obtained by fitting polynomial functions to the analytical results. These simple correction factors are shown to significantly improve the performance of the Stefan equation for several hypothetical soil freezing and thawing scenarios. Copyright (c) 2015 John Wiley & Sons, Ltd.