In this study, an analysis of wind-induced snow accumulation on a gable roof fitted with snow barrier railings is conducted using a one-way coupling method for wind and snow, alongside the k − kl − ω turbulence model. User-defined functions in ANSYS FLUENT (version 2020 R1) were used to implement the inflow boundary conditions, including wind profiles, turbulence energy, and snow phase volume fractions. The feasibility of the simulation method was verified through field measurements and numerical simulations of snow distribution characteristics on stepped flat roofs. Employing this validated method, this study simulated snow accumulation on gable roofs equipped with snow barrier railings and conducted an in-depth analysis of snow accumulation characteristics under varying installation angles of the snow barrier railings and wind direction angles. Comparative analyses revealed that at a 45° laying angle, the maximum erosion coefficient reached 0.5 within the X/H range of −3.6 to −3.4, while the minimum sedimentation coefficient of 1.03 was observed in the X/H range of −3.3 to −2.6. Notably, when the wind direction angle was 90°, the wind pressure coefficient on the windward side of the roof exhibited a relatively high value of −0.8, and it progressively diminished in a stepped fashion as it moved toward the leeward side, ultimately reaching a value of −0.1. The sedimentation area was maximized, covering 32.5% of the gable roof’s surface. Considering the snow-blocking effect and the uneven distribution of snow on roofs, the most effective snow-blocking performance was achieved with the snow barrier railings set at a 45° angle, while a wind angle of 90° was considered the most unfavorable wind angle condition.

Road surface grip during snowstorms is critical for traffic mobility and safety. It is important to incorporate surface grip as a quantitative indicator for effective decision-making in winter roadway maintenance. However, the timing of salt application and its influence on grip are rarely considered in decision-making to improve the efficiency of winter maintenance. To address this gap, data-driven approaches were developed to predict road surface grip (friction) for salt application in winter roadway maintenance. An advanced recurrent neural network (RNN) model, long short-term memory (LSTM), was developed with hyperparameter tuning. The LSTM model considers sequential effects of surface temperature, atmospheric condition, and salt application on the time-dependent evolution of road surface grip. Sensitivity analysis results show that road surface grip changes to a higher level more quickly when a 50–100 lb/mi higher salt application rate is applied as compared to current practice. The interaction effects of salt application and climate condition on road surface grip were further analyzed. It was found that the grip change became more sensitive to salt application rate when road surface temperatures were 4°C lower. The use of the LSTM model enables event-based decision-making for salt application in winter roadway maintenance.

Asphalt pavement performance deteriorates markedly under the influence of environmental factors due to its sensitivity to climatic conditions, such as temperature and precipitation. The degradation of asphalt pavements caused by freeze–thaw cycles is a significant concern, given that a substantial portion of China experiences frequent freezing. To this end, this research proposes a performance zoning methodology to mitigate freeze–thaw damage of asphalt pavements. The results indicate that ground temperature provides a more accurate metric than air temperature for evaluating the freeze–thaw effect on asphalt pavements. Employing a three-grade classification system based on the difference in the impact of freezing temperature on pavement structural damage, this study introduces the equivalent number of freeze–thaw cycles as the temperature zoning index. Additionally, annual cumulative precipitation under negative temperatures is incorporated as the precipitation zoning index. Utilizing the k-means clustering algorithm, asphalt pavements were categorized into four distinct freeze–thaw cycle performance zones. Zone I, characterized by significant temperature fluctuations and minimal precipitation, is especially vulnerable to temperature-induced fatigue cracking. Zone II faces a heightened risk of pothole distress due to minor temperature variations paired with significant precipitation. Moreover, Zones III and IV are severely affected by freeze–thaw distress, driven by frequent temperature changes and ample precipitation. This study provides critical insights for mitigating the adverse effects of freeze–thaw cycles on asphalt pavements in cold regions. The identification of vulnerable regions facilitates the establishment of design guidelines and maintenance practices to enhance pavement durability.

To investigate the deformation characteristics of coarse-grained sulfate–saline soil subgrades to the thermal responses of pavement structures in cold arid environments with significant diurnal temperature variations, this study focuses on a highway located in the Tarim Basin in Northwest China. Three test sections (K-I, K-II, and K-III) were constructed, each featuring distinct configurations of pavement base layers. The designs of these sections depend on the characteristics of the pore structure and cement content. Multifactor monitoring of moisture, heat, salt, and mechanics was conducted to evaluate the deformation behavior of the subgrade under varying thermal conditions. Section K-I with the insulated low-cement-dosage skeleton-dense gradation (insulated LC-SDG) base exhibits the smallest daily temperature amplitude of 3.1°C and low thermal sensitivity. Section K-III with the pavement base of high-cement-dosage suspension-dense gradation (HC-SusDG) displays the highest thermal sensitivity, recording an annual temperature amplitude that exceeds that of Section K-II (LC-SDG) and Section K-I by 2.5%–4.0%. This intensifies the phase transition in high-sulfate subgrade soils of Section K-III, where the latent heat released during crystallization slows the temperature decline at the subgrade surface during cooling periods. In Section K-III, pronounced dissolution-crystallization cycles of moisture and salt, coupled with steep thermal gradients, result in excessive moisture-salt accumulation beneath the upper impermeable layer, which is 6.9–15.4 times higher than that in Sections K-II and K-I. Consequently, the subgrade surface experiences a peak soil pressure of 229.6 kPa, cyclic pressure amplitudes of 3.7 kPa, and salt-induced pressure increments of 3.4 kPa in Section K-III. These stresses promote the accumulation of tensile strain, and during the optimal salt heaving period (December–February), the peak reaches only 212.8 × 10−6 at the subbase of Section K-I. The thermal regulation and cement content-optimization of pavement structures are critical for mitigating the deformation of sulfate–saline soil subgrades in cold arid regions. These findings provide theoretical guidance for enhancing regional pavement design and construction techniques.

To study the changing law of compressive strength for concrete with different saturation degrees, water, sodium chloride (NaCl), and sodium sulfate (Na2SO4) solutions are used to erode concrete specimens. Concrete specimens with 0%, 20%, 40%, 60%, 80%, and 100% saturation are obtained, and freeze–thaw tests with different numbers of freeze–thaw cycles (0, 30, 60, 90, 120, and 150 times) are conducted. Axial stress–axial strain curves are obtained by uniaxial compressive tests. The test results show a significant reduction in uniaxial compressive strength and an increase in axial peak strain with increasing saturation level. Following 150 freeze–thaw cycles, the strength loss rates for the water, NaCl, and Na2SO4 solution groups are 7.9%, 13.2%, and 29.7% for low (20%), 10.6%, 20.1%, and 41.7% for medium (60%), and 10.1%, 22.0%, and 45.9% for full (100%) saturation, respectively. The corresponding increases in axial peak strain are 22.9%, 39.1%, and 41.9% for low saturation, 21.4%, 29.8%, and 54.0% for medium saturation, and 17.0%, 34.4%, and 65.4% for complete saturation. It can be observed that the erosion of NaCl and Na2SO4 solutions results in more serious degradation of concrete strength. Freeze–thaw cycles further exacerbate the degradation of concrete strength, especially at high saturation levels. In this paper, a prediction model based on the binary medium strength criterion is proposed, which can effectively predict the strength of concrete after freeze–thaw cycles.

Unfrozen water content is one of the most prominent hydrothermal properties because it largely affects the hydraulic, physical, and mechanical behavior of frozen soils. However, the accurate estimation of unfrozen water contents under different subzero temperatures is challenging because it is affected by various soil properties. In this study, an artificial neural network (ANN) model (feedforward neural network) was proposed to estimate the unfrozen water content. A database with 1,033 unfrozen water content measurements and their corresponding influencing soil parameters was compiled from 16 published articles. The influencing soil parameters were ranked based on the Spearman correlation coefficient. After that, we investigated the effects of input soil properties and the numbers of neurons in the hidden layer on the estimated results. The unfrozen water contents estimated by the ANN models were evaluated with experimental results and four empirical models. Results suggested that the developed ANN models outperformed the empirical models, indicating that the ANN model has a superior ability to estimate the unfrozen water content of frozen soils.

Frozen soil contains initial defects such as cracks. In this study, four types of frozen soil specimens are prepared: intact specimens without prefabricated cracks and three precracked specimens with crack angles of 0°, 45°, and 90°. Dynamic compression experiments are conducted using a split Hopkinson pressure bar device to investigate the influence of prefabricated cracks and strain rates on the dynamic mechanical characteristics of frozen soil. The results indicated that the peak stresses of the intact and precracked frozen soil specimens increased with increasing strain rate. Compared with the intact frozen soil specimens, the prefabricated cracks resulted in a significant reduction in the peak stress. The stress distribution at the prefabricated crack boundary was calculated using the Griffith crack model. The dissipated energy is strongly related to the degree of fragmentation of frozen soil. As the dissipated energy increased, the fragmentation became more severe. The fragmentation of frozen soil is strongly influenced by the strain rate but shows insignificant sensitivity to the crack angle. Furthermore, to further reveal the dynamic failure process of the precracked frozen soil specimens, a finite-element model was constructed using ANSYS/LS-DYNA, and the Holmquist–Johnson–Cook model was employed to characterize the dynamic mechanical response of the precracked frozen soil specimens. The corresponding experiments were numerically simulated under experimental conditions. The results revealed that higher strain rates led to more complex crack propagation patterns, and the change in the crack angle affected the final failure mode.

Geopolymers have emerged as a promising eco-friendly alternative to conventional cement for soil stabilization in cold regions, offering both reduced carbon emissions and enhanced engineering performance. However, geopolymer-solidified soils (GSSs) exhibit significant degradation in thermal conductivity and mechanical strength subjected to subzero curing environment, posing challenges to practical applications. While laboratory testing remains essential for evaluating these properties, traditional experimental approaches face limitations such as prolonged durations, high resource demands, and difficulties in isolating multifactorial influences. To address these constraints, this study leveraged machine learning techniques, developing eight predictive models based on 1,440 experimental data sets to accurately estimate the thermal and mechanical behavior of GSS under subzero temperature curing. Utilizing the shapley additive explanations (SHAP) interpretability method, the research further identified and quantified the relative contributions of key input parameters governing GSS performance. Among the tested algorithms, the eXtreme Gradient Boosting (XGBoost) model demonstrated exceptional predictive accuracy (average R2 = 0.95) for thermal conductivity, unconfined compressive strength (UCS), shear strength, cohesion, and internal friction angle, with cohesion predictions showing the highest error margins, while thermal conductivity estimates were most precise. SHAP analysis revealed curing age as the dominant factor for thermal conductivity, cohesion, and internal friction angle (feature importance ≥0.39), whereas curing temperature (importance = 0.48) and normal stress (importance = 0.61) were critical for UCS and shear strength, respectively. These findings provide a robust data-driven framework for optimizing GSS mix designs in cold environments, ultimately supporting more sustainable and efficient geotechnical construction practices in permafrost and seasonal freeze–thaw regions.

Freeze–thaw (F-T) cycles are natural processes that affect the geotechnical properties of seasonally frozen soils. These soils cover about 50% of the Earth’s area. Clayey soils are common in cold regions. They are typically soft and weak. They are also more vulnerable to F-T cycles than coarse-grained soils. Therefore, studying the effects of F-T cycles on clayey soils is essential. This study utilized geopolymer binders composed of waste glass powder (WGP = 0%, 5%, 10%, 15%, and 20% by weight) and calcium carbide residue (CCR = 0%, 3%, 7%, 10%, and 13% by weight) to stabilize natural clay. Unconfined compressive strength (UCS) tests were conducted to find the optimum additive amounts. Direct shear and permeability tests were also performed. Microstructural changes were analyzed using scanning electron microscopy images, energy-dispersive X-ray spectroscopy, and BET tests. Samples were cured for 7 and 28 days. Then, they were subjected to F-T cycles in a closed system (F-T cycles = 0, 3, 6, 9, and 12). The sample with 15% WGP and 7% CCR showed the best performance. After 12 cycles, its UCS decreased by only 15%. Geopolymer formation altered the soil pore distribution in such a way that the total pore volume increased and the average pore diameter decreased. In contrast, F-T cycles had the opposite effect.

High-speed railway stations with long-span steel structures are vulnerable to severe climatic conditions in cold regions, particularly when temperature differences, snow loads, and seismic events occur concurrently. This study develops a finite-element model of a large-span steel roof in a high-seismic, cold climate zone to assess the combined effects of extreme temperature differences, snow loads, and earthquakes. Results reveal that under coupled extreme loading, key roof components experience significant stress increases, and midspan vertical displacements exceed 450 mm—nearly double the design deflection limit of 240 mm. These deformations pose serious structural risks, including excessive stress concentration at critical joints and an increased likelihood of progressive collapse, particularly in the midspan regions. The findings highlight the need to revise current design codes to consider load combinations typical in cold-seismic environments and propose engineering strategies, such as enhanced joint design and snow load monitoring systems, to improve structural resilience.