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Low-Temperature Cracking and Improvement Methods for Asphalt Pavement in Cold Regions: A Review
The advantages of asphalt pavement in terms of driving comfort, construction efficiency, and ease of maintenance have established it as the predominant choice for high-grade pavements at present. However, being highly sensitive to temperature and stress, asphalt performance is significantly influenced by external environmental conditions and loading, making it susceptible to various distress phenomena. Particularly in high-latitude regions, asphalt pavement cracking severely limits asphalt pavement’s functional performance and service lifespan under cold climatic conditions. To enhance the low-temperature cracking resistance of asphalt pavement in cold regions, tools such as VOS viewer 1.6.20 and Connected Papers were utilized to systematically organize, analyze, and summarize relevant research from the past 40 years. The results reveal that temperature shrinkage cracks and thermal fatigue cracks represent the primary forms of asphalt pavement distress in these regions. Cracking in asphalt pavement in cold regions is primarily influenced by structural design, pavement materials, construction technology, and climatic conditions. Among these factors, surface layer stiffness, base layer type, and the rate of temperature decrease exert the most significant impact on cracking resistance, collectively accounting for approximately 45.4% of all cracking-related factors. The low-temperature performance of asphalt pavement can be effectively improved through several strategies, including adopting full-thickness asphalt pavement with a skeleton-dense structure or reduced average particle size, incorporating functional layers, appropriately increasing the thickness of the upper layer and the compaction temperature of the lower layer, utilizing continuous surface layer construction techniques, and applying advanced materials. High-performance modifiers such as SBR and SBS, nanomaterials with good low-temperature performance, and warm mixing processes designed for cold regions have proven particularly effective. Among various improvement methods, asphalt modification has demonstrated superior effectiveness in enhancing the deformation capacity of asphalt and its mixtures, significantly boosting the low-temperature performance of asphalt pavements. Asphalt modification accounts for approximately 50% of the improvement methods evaluated in this study, with an average improvement in low-temperature performance reaching up to 143%. This paper provides valuable insights into the underlying causes of cracking distress in asphalt pavements in cold regions and offers essential guidance for improving the service quality of such pavements in these challenging environments.
Journal Article
Coldest places on the planet
\"Simple text and full-color photographs describe the coldest places on the planet\"-- Provided by publisher.
Book
Research on temperature field and frost damage prediction of highway tunnels in cold regions considering MCP method
The variation in wind speed outside tunnels in cold regions significantly impacts the tunnel temperature field. This study introduces the Measurement Correlation Prediction (MCP) method to establish a wind dataset for the external environment of tunnels in cold regions. The correlation model is based on the Weibull scale method. Results indicate that for the SSW (dominant wind direction) sector, the relative error between the wind data obtained from the Weibull scale model and actual wind data is 4.88%, demonstrating the high reliability of the model. According to the wind dataset, the average annual wind speed for the subsequent 30 years is predicted to be 3.46 m·s
− 1
, and the maximum annual wind speed with a 30-year return period is 4.52 m·s
− 1
. These findings highlight the risk of frost damage in the drainage ditch at the bottom of the tunnel, providing crucial information for the prevention and control of frost damage during tunnel operation. Additionally, this study investigates the influencing factors of frost damage in tunnels in cold regions, elucidating the relationships between the thermophysical properties of surrounding rock, ground temperature, airflow factors, and the frost depth at the bottom of tunnel (
X
). A neural network-based frost damage prediction model was developed using these factors as inputs. The model offers valuable insights for enhancing the operational safety and maintenance strategies of tunnel in cold region.
Journal Article
Active and Passive Optimization of the Indoor Thermal Environment of Rural Dwellings in Hohhot Under Clean Heating in Severe Cold Regions
In the severely cold regions of northern China, large-scale clean heating retrofits in rural areas face critical problems, including substandard indoor thermal environments, excessive energy consumption, and prohibitive operating costs. To address these challenges, this study focuses on rural residences in Hohhot as the research subject. Field measurements were conducted throughout the heating season in a typical rural house in Hohhot, a representative city with severe cold weather, to collect indoor/outdoor thermal parameters and real-time operational data of an air-source heat pump (ASHP). A dynamic simulation platform was established using TRNSYS 18. The optimization scheme integrates passive envelope retrofitting (ground insulation improvement and energy-efficient windows) with the active optimized control of the ASHP system. Indoor thermal comfort was evaluated using the Predicted Mean Vote (PMV) index. The results show that the ASHP exhibits excellent heating effectiveness and economic viability, making it the preferred technology for rural residences in Hohhot and similar regions. After implementing the active–passive scheme, the proportion of time with comfortable indoor conditions in rural houses surges from 34.1% to 84.1%, while during the severe cold period, this proportion increases from 16.97% to 61%. The indoor thermal comfort index shifts from its previous state to the baseline comfort range of −1.0 to 0. The total heating energy consumption decreased from 18,646 kWh to 15,861 kWh, and the seasonal operating cost dropped from 3207 to 2579.3 RMB, achieving an overall reduction of 19.6% in both energy and costs. The proposed active–passive synergistic optimization scheme simultaneously improves the indoor thermal environment and reduces heating energy consumption, overcoming the limitations of single-measure retrofits. This study fills the research gap on the quantitative evaluation of active–passive synergy for rural clean heating in severely cold regions, providing a theoretical basis and technical support for clean heating retrofits in Hohhot and Inner Mongolia, facilitating low-carbon and efficient rural clean heating in northern China.
Journal Article
Ice and snow in the Cold War : histories of extreme climatic environments
\"The history of the Cold War has focused overwhelmingly on statecraft and military power, an approach that has naturally placed Moscow and Washington center stage. Meanwhile, regions such as Alaska, the polar landscapes, and the cold areas of the Soviet periphery have received little attention. However, such environments were of no small importance during the Cold War: in addition to their symbolic significance, they also had direct implications for everything from military strategy to natural resource management. Through histories of these extremely cold environments, this volume makes a novel intervention in Cold War historiography, one whose global and transnational approach undermines the simple opposition of 'East' and 'West'-- Provided by publisher.
Book
Stability analysis and prediction of hazardous rock mass in cold regions based on hybrid algorithm model
In the complex geological environments of cold regions, traditional methods struggle to address the multifactorial coupling and nonlinear dynamic evolution of hazardous rock mass driven by freeze‒thaw cycles. To overcome these challenges, this study investigates the applicability and optimization of intelligent prediction models tailored to cold regions. A long-term stability prediction framework is constructed by integrating the freeze–thaw–gravity coupling mechanism mechanism. Unlike generic hybrid models, this research systematically compares and optimizes various metaheuristic algorithms (SSA, PSO, GA) coupled with neural networks to identify an effective strategy for the high-dimensional, nonlinear characteristics of rock mass in these regions. Focusing on hazardous rock mass in western China, six primary influencing factors—cohesion, freezing depth, lowest temperature, freezing load, sunshine duration, and foot of slope displacement—were selected on the basis of the typical freeze–thaw–gravity coupling mechanism damage mechanism. Key control parameters were identified via gray relational analysis (GRA), and data normalization was applied to enhance model generalizability. The evaluation results demonstrate that hybrid algorithm models outperform traditional single-algorithm models for the investigated cases, with improved prediction accuracy and adaptability under freeze-thaw-dominated conditions. Specifically, the SSA-BP model reduced the root mean square error (RMSE) by approximately 30% compared with the standalone BP model, whereas the mean absolute error (MAE) and mean squared error (MSE) decreased by 28% and 35%, respectively, and achieved a goodness-of-fit with measured data exceeding 90%. Moreover, the PSO-BP model improved computational efficiency by approximately 40% while maintaining prediction accuracy, rendering it suitable for real-time monitoring and rapid warning scenarios. These findings indicate that hybrid algorithm models partially alleviate the limitations of single models—such as poor generalizability and susceptibility to local optima—by incorporating global optimization mechanisms and adaptive parameter adjustment, thereby demonstrating improved robustness and potential engineering-oriented applicability.
Journal Article
Multi-source data–driven prediction of cold-region slope failure using an SSA-PNN optimized stepwise reduction approach
Cold-region slopes are highly susceptible to instability due to freeze–thaw–creep coupling, which gradually degrades the mechanical properties of geomaterials and accelerates the formation of slip surfaces. To address the limitations of conventional strength reduction methods that fail to capture progressive failure mechanisms, this study proposes an integrated framework that combines the Stepwise Reduction Method (SRM) with a machine learning model based on a Sparrow Search Algorithm–optimized Probabilistic Neural Network (SSA-PNN). A multi-source dataset was established by incorporating 42 field monitoring segments, 78 numerical simulation samples, and laboratory tests of mechanical degradation under 0–60 freeze–thaw cycles, covering 15 environmental, material, structural, and response features. The model performance was evaluated using classification and regression tasks, with safety factor (FS) as the reference label. Results show that the SSA-PNN achieved an accuracy of 87.5%, macro-F1 of 0.869, weighted F1 of 0.877, macro-AUC of 0.979, and Brier score of 0.056 in classification, while in regression it obtained MAE = 0.041, RMSE = 0.053, and R
2
= 0.871, consistently outperforming benchmark models such as XGBoost, SVM, and Logistic Regression. Notably, in the critical stability interval (1.30 ≤ FS < 1.50), the SSA-PNN reduced misclassification rates by 12.4% compared with the conventional PNN, demonstrating a marked improvement in distinguishing borderline states. These findings confirm that the SRM–SSA-PNN framework effectively characterizes the spatiotemporal evolution of slope degradation under freeze–thaw effects, enhances the interpretability of instability mechanisms, and provides a reliable basis for risk assessment, intelligent monitoring, and early warning of geohazards in cold regions.
Journal Article