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"Cold regions"
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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
Coldest places on the planet
\"Simple text and full-color photographs describe the coldest places on the planet\"-- 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
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
The Use of Horizontal Shading Devices to Alleviate Overheating in Residential Buildings in the Severe Cold Region and Cold Region of China
Global warming is resulting in higher summer indoor temperatures in the severe cold region and cold region of China, and this is affecting thermal comfort. Local building design codes consider these regions as cool in summer, and do not consider the phenomenon of overheating or propose countermeasures. This paper studied the possibility of overheating in residential buildings in these areas. It suggested alleviating this phenomenon using external horizontal shading, and discussed how to integrate thermal comfort into the building design and save energy consumption. The IESVE software was used to simulate 18-storey residential buildings with natural ventilation in Yichun, Harbin, Shenyang, Dalian, and Beijing, and to calculate the change in indoor operative temperature. Horizontal shading was designed for case study building to attempt to alleviate the overheating phenomenon in summer. The results showed that the case study building in the five cities experienced different degrees of overheating. External horizontal shading was successful in reducing indoor overheating, especially in the severe cold B and C zones and the cold A and B zones. The relevant building codes should be modified to take this into account. Reasonable design of horizontal shading can effectively reduce energy consumption, particularly when compared with air-conditioned buildings.
Journal Article
Study on effects of the train-induced airflow on the temperature field of single-track high-speed railway tunnels in cold regions
High-speed trains bring cold air into tunnels, significantly affecting the temperature field in cold-region tunnels, leading to risks of lining freeze-thaw damage, track deformation, and safety hazards, which is detrimental to thermal insulation design. Using the Kunlunshan Tunnel as a case study and based on unsteady flow theory, this study constructed the Navier-Stokes equations and proposed a theoretical calculation method for train-induced airflow. Dynamic mesh technology was adopted to develop an “equivalent wind speed” train-induced-airflow-temperature coupling computational model. The main idea of this model is to divide the effect of external wind speed on the tunnel into three distinct phases: the train-induced airflow phase, the residual airflow phase, and the natural wind phase. During each phase, the changes in wind speed are governed by the principle of conservation of cold air volume and converted into a steady airflow equivalent for that specific phase. This model was used to investigate the effects of train frequency, ground temperature of surrounding rock, external temperature, and external wind speed on the temperature field variation in single-track high-speed railway tunnels in cold regions. After verification, the temperature field inside the tunnel calculated by the “equivalent wind speed” method was in good agreement with the measured data, and can be used for subsequent numerical simulation calculations. The results show that when the interval between two trains is less than 20 min, the average temperature in the tunnel decreases by approximately 1.71 ℃ due to the significant impact of train-induced airflow. The impact of the ground temperature of surrounding rock on the longitudinal temperature gradient in cold-region tunnels ranges from 0.059 ℃ to 1.13 ℃ for every 100 m, and the ranges for external temperature and external wind speed are 0.080 ℃ to 2.286 ℃ and 0.002 ℃ to 1.134 ℃, respectively.
Journal Article