The appearance of ground frost is of vital importance in construction and maintenance of roads in cold climates. Frost often causes ground heave and subsequent road damage, which must be taken into account in designing the road structure. Frost depth, pavement temperature, and freezing/thawing cycles are also important for estimating the frequency of road maintenance and treatment. Various analytical, numerical, and empirical models have been developed to estimate the surface temperature of the pavement and to model the heat flow in the underlying layers. The pavement surface experiences a variety of intricate nonlinear heat transfer mechanisms during winter, making it challenging to accurately model the surface boundary. Dynamic variation of parameters such as cloud cover and traffic density during the modeling period introduces additional complexity. To address this challenge, we have established an experimental setup in Luleå, Sweden, to measure pavement profile temperatures during the winter season. Additionally, we have developed a Finite Difference Model that utilizes local weather data including dynamic cloud cover, and which also takes traffic into account. The experimental and simulation findings demonstrate how the impact of surface temperature fluctuations diminishes and, more or less, vanishes for depths more than 55 [cm] below the pavement surface. The Finite Difference Model presented in this study exhibits the ability to forecast the pavement profile temperatures, including the surface temperature based on weather conditions, with acceptable precision for at least 3 days. As a consequence, a reasonable assessment of pavement layer conditions appears feasible based on local weather conditions, and the model can serve as a useful tool for planning road maintenance and construction in cold regions.

Pressure-sinkage tests for determining vehicle sinkage on soft soils can be done using a bevameter. In this study, pressure-sinkage tests were performed on snow, which, like soil, is a granular material. However, unlike soil, snow layers are inhomogeneous with varying properties. For tracked vehicles, the shape of the track print is rectangular, which is why rectangular plates are often used for pressure-sinkage tests. The aim of this study was to see if smaller circular plate or smaller rectangular plates can be used instead of larger rectangular plates, and to understand the possible limitations of using small plates. Radius for the circular plate was chosen to be equal to the width of the rectangular plate. Three measuring sessions were performed at different locations during different snow conditions using circular pressure plates and rectangular pressure plates of different aspect ratios. The results show that smaller rectangular plates can be used if the width of the plates remains the same, or circular plates can be used if the radius of the circular plate is equal to the width of the rectangular plate. Limitation comes with increasing pressure, which occurs more quickly with larger-area plates, as larger plates sense solid ground more rapidly than smaller plates. To avoid this, snowpack thickness should be a minimum of five times thicker than maximum sinkage.

We experimentally demonstrate the presence of a capillary bridge in the contact between an ice particle and a smooth aluminum surface at a relative humidity of approximately 50% and temperatures below the melting point. We conduct the experiments in a freezer with a controlled temperature and consider the mechanical instability of the bridge upon separation of the ice particle from the aluminum surface at a constant speed. We observe that a liquid bridge forms, and this formation becomes more pronounced as the temperature approaches the melting point. We also show that the separation distance is proportional to the cube root of the volume of the bridge. We hypothesize that the volume of the liquid bridge can be used to provide a rough estimate of the thickness of the liquid layer on the ice particle since in the absence of other driving mechanisms, some of the liquid on the surface must have been pulled to the bridge area. We show that the estimated value lies within the range previously reported in the literature. With these assumptions, the estimated thickness of the liquid layer decreases from nearly 56 nm at T = −1.7°C to 0.2 nm at T = −12.7°C. The dependence can be approximated with a power law, proportional to (TM − T)−β, where β < 2.6 and TM is the melting temperature. We further observe that for a rough surface, the capillary bridge formation in the considered experimental conditions vanishes.

Despite the significance of snow in various cryospheric, polar, and construction contexts, more comprehensive studies are required on its mechanical properties. In recent years, the utilization of μ CT has yielded valuable insights into snow analysis. Our objective is to establish a methodology for mapping density-dependent material properties for dry manufactured snow within the density range of 400–600 kg/m 3 utilizing μ CT imaging and step-wise, quasi-static, mechanical loading. We also aim to investigate the variations in the structural parameters of snow during loading. The three-dimensional (3D) structure of snow is captured using μ CT with 801 projections at the beginning of the experiments and at the end of each loading step. The sample is compressed at a temperature of − 18 o C using a constant rate of deformation (0.2 mm/min) in multiple steps. The relative density of the snow is determined at each load step using binary image segmentation. It varies from 0.44 in the beginning to nearly 0.65 at the end of the loading, which corresponds to a density range of 400–600 kg/m 3 . The estimated modulus and viscosity terms, obtained from the Burger’s model, show an increasing trend with density. The values of the Maxwell and Kelvin–Voigt moduli were found to range from 60 to 320 MPa and from 6 to 40 MPa, respectively. Meanwhile, the viscosity values for the Maxwell and Kelvin–Voigt models varied from 0.4 to 3.5 GPa-s, and 0.3–3.2 GPa-s, respectively, within the considered density range. In addition, Digital Volume Correlation (DVC) was used to calculate the full-field strain distribution in the specimen at each load step. The image analysis results show that, the particle size and specific surface area (SSA) do not change significantly within the studied range of loading and densities, while the sphericity of the particles is increased. The grain diameter ranges from approximately 100 μ m to nearly 400 μ m, with a mode of nearly 200 μ m. The methodology presented in this study opens up a path for an extensive statistical analysis of the material properties by experimenting more snow samples.

The mechanical properties of natural snow play a crucial role in understanding glaciers, avalanches, polar regions, and snow-related constructions. Research has concentrated on how the mechanical properties of snow vary, primarily with its density; the integration of cutting-edge techniques like micro-tomography with traditional loading methods can enhance our comprehension of these properties in natural snow. This study employs CT imaging and uniaxial compression tests, along with the Digital Volume Correlation (DVC) to investigate the density-dependent material properties of natural snow. The data from two snow samples, one initially non-compressed (test 1) and the other initially compressed (test 2), were fed into Burger’s viscoelastic model to estimate the material properties. CT imaging with 801 projections captures the three-dimensional structure of the snow initially and after each loading step at -18C, using a constant deformation rate (0.2 mm/min). The relative density of the snow, ranging from 0.175 to 0.39 (equivalent to 160–360 kg/m), is determined at each load step through binary image segmentation. Modulus and viscosity terms, estimated from Burger’s model, exhibit a density-dependent increase. Maxwell and Kelvin–Voigt moduli range from 0.5 to 14 MPa and 0.1 to 0.8 MPa, respectively. Viscosity values for the Maxwell and Kelvin–Voigt models vary from 0.2 to 2.9 GPa-s and 0.2 to 2.3 GPa-s within the considered density range, showing an exponent between 3 and 4 when represented as power functions. Initial grain characteristics for tests 1 and 2, obtained through image segmentation, reveal an average Specific Surface Area (SSA) of around 55 1/mm and 40 1/mm, respectively. The full-field strain distribution in the specimen at each load step is calculated using the DVC, highlighting strong strain localization indicative of non-homogeneous behavior in natural snow. These findings not only contribute to our understanding of natural snow mechanics but also hold implications for applications in fields such as glacier dynamics and avalanche prediction.

Winter road maintenance is important for preventing accidents and enabling mobility. If the road friction gets low, there is a higher risk of road accidents. Therefore, it is vital to have information about road friction levels. Traditionally this is done by dedicated vehicles; however, using friction information from floating car data (FCD) would be more beneficial, as the coverage both in time and space increases. In this investigation, road friction data from three FCD suppliers, using only one test vehicle each, has been compared with a continuous method of road friction measurement. The test has been conducted on proving grounds covered with ice and snow, and on public roads covered with water, ice, snow, and slush; thereby both high friction and low friction surfaces have been evaluated. The investigation shows that the FCD provides a continuous method of friction measurement and is closer to the reality of road friction experienced by road users.

Road surface temperature (RST) plays an essential role in analyzing road surface conditions during winter in countries with adverse winter climates. A reduction in RST can have a negative impact on road safety due to decreasing vehicle grip on the road surface. Therefore, decision makers need to monitor low surface temperatures and plan for winter road maintenance. However, RST sensors can fail for different reasons, such as power outages. RST sensor failure will lead to lack of information about the road surface, which can be problematic, especially for critical road segments. Hence, the novelty of this study is to use a deep learning algorithm to predict RSTs in road segments if a sensor fails at a station using historical data from two other road stations. The mean absolute error in the proposed model is 0.453 and the model explains 98.6% of observations. In addition, since the adjustment of deep learning parameters (e.g., hidden layers, optimizer, activation function, etc.) is associated with epistemic uncertainty, a semiquantitative approach is developed for uncertainty assessment. With this approach, the most important and uncertain parameters in RST prediction models can be identified. The results have shown that the optimizer is the most uncertain and important parameter.