The EU aims to accelerate the transition to a circular economy to reduce waste and its environmental impact since the linear economy model imposes significant environmental burdens. Resources are finite, and the consequences of environmental degradation, climate change, and disaster risks cannot be ignored. The construction industry accounts for over 38% of waste in Europe, so it has become inevitable to activate road maintenance as one of the enablers for the transition towards circular economy strategies.

This study aims to explore circular economy (CE) frameworks, indicators, and sustainable maintenance and rehabilitation (M&R) materials and methods. For this purpose, a systematic review has been conducted to explore the transition towards the circular economy and its interconnectivity with the road Pavement Management System (PMS). Scopus and Web of Science (WoS) are the primary databases that have been explored. Google Scholar also has been explored to complete the references. By examining and analyzing 62 papers from the mentioned databases, 26 relevant papers have been selected. By examining the previous practices of CE implementation, the review highlights how pavement maintenance of road have evolved to become more circular. This study highlights different CE frameworks, indicators, and sustainable maintenance materials, methods, and technologies that can facilitate circular PMS practices.

In the digital era, all sectors of society are moving towards increased connectivity, including the transport system. Digitization is essential for achieving Vision Zero, a future without road traffic casualties and with minimal environmental impact. Connected vehicle (CV) data, currently shared within fleets of the same brand, will soon be exchanged between different brands. However, interpreting parameters such as tire-road friction (TRF), estimated using supplier-specific algorithms, remains a challenge. This study investigates similarities and differences between two TRF estimation methods developed by different CV data suppliers. Both approaches use tire slip and internal vehicle signals. Data were collected in Sweden on public roads, covering around 100,000 kilometres, and span two reference periods (April, July, and October in 2023 and 2024) and three winter seasons (December to March) from 2022 to 2025. A comparison using confusion matrices is performed on road segments up to 550 m in length, where 1 h measurements from both suppliers are available. Accuracy in classifying high and low TRF ranges between 85% and 93% during winter. Differences are related to road weather conditions and the suppliers' use of TRF limits. Despite these variations, it is feasible to fuse TRF data from multiple suppliers, particularly for short segments.

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.

Ground thermal regime in cold regions is influenced by seasonal snow cover, which acts as an insulating layer influencing the heat transfer between the atmosphere and the underlying soil. The thermal properties of the snow change with different environmental conditions, playing a crucial role to determine the thermal state of the sub-surface soil. Previous research in this field have faced challenges to accurately characterize thermal properties of seasonal snowpacks under varying spatial and meteorological conditions. To address this issue, two experimental field setups were constructed in Luleå, Sweden, to observe the temperature distribution of the snowpack and the ground sub-surface soil. The first experiment studied a naturally accumulated, undisturbed snowpack. The second experiment was conducted on a roadside ditch where the snowpack consists of a combination of natural accumulated snow and plowed snow from the adjacent road. In this research, heat transfer processes at both field sites were monitored over a winter season each to better understand the complex relationship between snow cover properties and sub-surface thermal regime. Furthermore, thermal conductivity of a basal layer in each snowpack was calculated over a time period, based on the field measurements. The results showed that the history of snow deposition, meteorological conditions, and changes in soil moisture impact the metamorphism process within the snowpack, thereby altering the structure of the layers of snowpack and its influence on the thermal regime of the sub-surface soil. The findings of this research have important applications in various sectors, from mining to road maintenance and agriculture.

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.