Cold Regions Science and Technology最新文献

{"title":"Simulating winter maintenance efforts: A multi-linear regression model","authors":"","doi":"10.1016/j.coldregions.2024.104307","DOIUrl":"10.1016/j.coldregions.2024.104307","url":null,"abstract":"<div><p>Winter Road Maintenance (WRM) ensures road mobility and safety by mitigating adverse weather conditions. Yet, it is costly and environmentally impactful. Balancing these expenses, impacts, and benefits is challenging. Simulating winter maintenance services offers a potential new tool to find this balance. In this paper, we analyze Norway's WRM of state roads during the 2021–2022 winter season and propose an effort model. This model forms the computational core of the simulation, predicting the number of plowing, salting, and plowing-salting operations at any given location over the road network. This is a multi-linear regression model based on the Gaussian/OLS method and comprises three sub-models, one for each of the aforementioned operations. The key explanatory variables are: 1) level of service (LOS), 2) road width, 3) height above mean sea level, 4) Average Annual Daily Traffic (AADT), 5) snowfall duration, 6) snow depth, 7) number of snow days (fallen snow and drifting snow), 8) number of freezing-rain days, 9) number of cold days and 10) number of days with temperature fluctuations. The overall effort prediction accuracy for the winter season 2021–2022 was 71 %. The independent variables, the model's outcomes, and its results when applied to simulate the effects of LOS downgrading on a particular road stretch and estimating CO₂ emission over the whole network, are discussed.</p></div>","PeriodicalId":10522,"journal":{"name":"Cold Regions Science and Technology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0165232X24001885/pdfft?md5=80423bfe3203bba0e05b915dd8a8285a&pid=1-s2.0-S0165232X24001885-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142136078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental study on the technology optimization of clear ice thickness detection on horizontal cold plate surface by using microwave resonance","authors":"","doi":"10.1016/j.coldregions.2024.104308","DOIUrl":"10.1016/j.coldregions.2024.104308","url":null,"abstract":"<div><p>The accumulation of snow and ice has the potential to have a negative impact on numerous industries if it is not accurately detected and processed in real-time. Microwave resonators have gained interest as durable and reliable ice detectors. To detect the thickness of clear ice slices on a horizontal cold plate surface, a capacitively coupled split-ring resonant sensor was experimentally investigated. To ascertain the efficacy of the sensor, plexiglass with similar relative permittivity to ice was firstly tested. The effect of the plexiglass plate thickness on the resonance amplitude of the transmission scatter parameter was found to be monotonic in the range of 16.8 mm thickness, thereby demonstrating the ability of the sensor to accurately measure plate thickness. Then, the effect of different thicknesses of clear ice slices within 17.0 mm on the resonance parameters was tested under constant temperature. The resonant amplitude decreased by 46.55% from −4.13 dB to −6.05 dB, as the thickness of the clear ice slice gradually increased from 2.5 mm to 17.0 mm. A model for the detection of ice thickness based on the analysis of theoretical principles and experimental data was developed. The ice thickness could be detected accurately within a range of 17.0 mm at temperatures between −3 and −20 °C, with a maximum deviation of 5.66% in the detection of ice thickness. This study validates the application of the sensor to detect ice thickness, such as on ships, roads and aircraft.</p></div>","PeriodicalId":10522,"journal":{"name":"Cold Regions Science and Technology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142163949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative analysis of machine learning techniques for accurate prediction of unfrozen water content in frozen soils","authors":"","doi":"10.1016/j.coldregions.2024.104304","DOIUrl":"10.1016/j.coldregions.2024.104304","url":null,"abstract":"<div><p>Unfrozen water content (UWC) plays a critical role in determining the thermal, hydraulic, and mechanical properties of frozen soils. Existing empirical, semi-empirical, and theoretical models for UWC estimation have limitations in terms of accuracy as well as generalizability. To address these challenges, the present study explored the application of six machine learning techniques to predict UWC in frozen soils: Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), and Backpropagation Neural Network (BPNN). Considering the UWC hysteresis phenomenon between the freezing and thawing processes, experimental UWC data collected from the literature were separated into two sub-datasets: freezing branch dataset (FBD) and thawing branch dataset (TBD). Based on that, a comprehensive framework integrating Bayesian optimization and 10-fold cross-validation was established to optimize the six models' hyperparameters and to evaluate their performance. The results highlighted significant variations in the predictive capability among the six machine learning models, with ensemble methods (i.e., RF, XGBoost, LightGBM) generally demonstrating superior accuracy. Feature importance analysis, robustness checks, and uncertainty quantification further elucidated the strengths and limitations of each model. The present study provides profound insights into the selection and application of machine learning models for accurately modeling the properties of frozen soils for cold regions science and engineering.</p></div>","PeriodicalId":10522,"journal":{"name":"Cold Regions Science and Technology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142095841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High strain-rate behavior of polycrystalline and granular ice: An experimental and numerical study","authors":"","doi":"10.1016/j.coldregions.2024.104295","DOIUrl":"10.1016/j.coldregions.2024.104295","url":null,"abstract":"<div><p>We study the stress–strain response of two different types of ice, viz. polycrystalline ice and granular ice, between −1° – 0 °C over a strain-rate range of <span><math><mn>100</mn><mspace></mspace><msup><mi>s</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup></math></span> to <span><math><mn>300</mn><mspace></mspace><msup><mi>s</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup></math></span> employing the split Hopkinson pressure bar (SHPB). Polycrystalline ice samples, prepared by freezing water in plastic moulds, exhibit a compressive strength ranging from 7 to 10 MPa within the considered strain-rate range. The strain at peak stress remains below 0.2%, indicating brittle behavior. The stress-strain curve of polycrystalline ice displays a prolonged tail, suggesting that the damaged ice specimen retains some strength. High-speed imaging during tests reveals the damage mechanism in ice is fragmentation and axial splitting. A user subroutine based on the Johnson–Holmquist II (JH-2) model is implemented in the commercial finite element (FE) software ABAQUS to predict ice's response at high strain-rates, which captures the softening present in the experimental stress–strain curve. Intact strength parameters and strain-rate sensitivity constants in the JH-2 model are determined from our experimental data and literature results, ensuring alignment with experimental peak stress. Fractured strength and damage evolution parameters are determined by matching post-peak responses from simulations to experiments. Temporal damage evolution from FE simulations aligns well with high-speed images from experiments, providing additional validation. Extending the study to granular ice, samples are prepared by crushing polycrystalline ice and refreezing it. The compressive strength of granular ice at a nominal strain-rate of <span><math><mn>200</mn><mo>±</mo><mn>50</mn><mspace></mspace><msup><mi>s</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup></math></span> is found to be <span><math><mn>4</mn><mo>±</mo><mn>0.7</mn></math></span> MPa. The granular ice, which is a mixture of polycrystalline ice and voids, is homogenized using rule-of-mixture to obtain the elastic properties. The FE simulation results utilizing the JH-2 parameters that we determine matches well with the experimental data, demonstrating that the JH-2 model is well suited to predict the high strain-rate behavior of both types of ice.</p></div>","PeriodicalId":10522,"journal":{"name":"Cold Regions Science and Technology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142095842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring machine learning models to predict the unfrozen water content in copper-contaminated clays","authors":"","doi":"10.1016/j.coldregions.2024.104296","DOIUrl":"10.1016/j.coldregions.2024.104296","url":null,"abstract":"<div><p>The article provides new insights into predicting unfrozen water content(u_nf) in clays contaminated with copper. The objectives of this study included creating machine learning prediction models based on Gaussian Process Regression (GPR), Support Vector Machine (SVM), and Random Forest (RF) algorithms. These models were developed using seventeen soil physicochemical parameters. A total of 575 experimental observations of unfrozen water content, determined by the DSC method over a temperature range of −23 °C to −1 °C, were analyzed. The findings suggest that the unfrozen water content in copper-contaminated clays can be most accurately predicted using the Random Forest model, which achieved a high correlation coefficient (<em>R</em> = 0.962). This model demonstrated greater effectiveness than existing empirical models in estimating unfrozen water content in these soils. Further research should focus on exploring alternative machine learning techniques to improve predictions of unfrozen water content.</p></div>","PeriodicalId":10522,"journal":{"name":"Cold Regions Science and Technology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142077519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A global analysis of ice phenology for 3702 lakes and 1028 reservoirs across the Northern Hemisphere using Sentinel-2 imagery","authors":"","doi":"10.1016/j.coldregions.2024.104294","DOIUrl":"10.1016/j.coldregions.2024.104294","url":null,"abstract":"<div><p>As existing global lake ice studies have predominantly focused on medium to large lakes, and reservoir ice studies have been limited to regional scales, very few studies of ice phenology have combined both lakes and reservoirs of different sizes. This study aims to characterize the freeze-up and break-up dates of 3702 lakes and 1028 reservoirs from 1 to 31,000 km² across the Northern Hemisphere, and to analyze spatial patterns and relationships between ice phenological dates and driving factors. The freeze-up and break-up dates of these water bodies were retrieved from Sentinel-2 imagery using an ice detection algorithm through the Google Earth Engine platform from 2019 to 2023. The algorithm was verified by comparing phenology dates with an independent database based on observations from passive microwave sensors, with a mean absolute error of 18 days for both freeze-up and break-up dates. This newly established ice phenology database along with various geographic, morphometric, and climatic characteristics of the water bodies, was used to develop a random forest model for predicting ice phenology dates. While the predictive model performance is at a fair level (mean absolute error of 12 days for both freeze-up and break-up), challenges were encountered in certain high-elevation areas where cloudy conditions as well as black ice resulted in delayed freeze-up dates. Among the variables included in the random forest model, latitude and accumulation of freezing degree days were identified as the main drivers of ice phenology dates. Despite the challenges of applying a single, straightforward method on a global scale, this study has allowed the creation of a vast and comprehensive database of lake and reservoir freeze-up and break-up dates that can be used by the community to further analyze ice patterns.</p></div>","PeriodicalId":10522,"journal":{"name":"Cold Regions Science and Technology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating equivalent elastic properties of frozen clay soils using an XFEM-based computational homogenization","authors":"","doi":"10.1016/j.coldregions.2024.104292","DOIUrl":"10.1016/j.coldregions.2024.104292","url":null,"abstract":"<div><p>This study addresses the challenge of estimating the elastic properties of heterogeneous frozen clay soils by introducing a comprehensive approach that combines analytical and numerical models. The frozen clay soil is treated as a mixture composed of frozen clay-water composites and nonclay mineral inclusions. An inversion algorithm is employed to deduce the elastic properties of the matrix (clay-water composites) of two artificially frozen sandy clay samples with known temperature-dependent elastic properties. Subsequently, a two-dimensional numerical simulation using the eXtended Finite Element Method (XFEM) is conducted to carry out numerical homogenization by considering the imperfect bond among frozen clay-water composites and nonclay minerals. The numerical homogenization model offers insights into the temperature-dependent behavior of the interface stiffness parameter. The numerical homogenization results are compared with conventional numerical homogenization approaches like the FEM, which rigidly defines the bonding between inclusions and the matrix. The comparison indicates that the neglect of imperfect bonds among clay-water composites and nonclay minerals will lead to unrealistic outcomes in cases with a high fraction of inclusions. This integrated approach advances the understanding and prediction of elastic properties of frozen clay soils by considering their heterogeneous nature.</p></div>","PeriodicalId":10522,"journal":{"name":"Cold Regions Science and Technology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0165232X24001733/pdfft?md5=65158f32783ff4f1d2f037e0db30d6b7&pid=1-s2.0-S0165232X24001733-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141985195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Freeze-thaw landslide susceptibility assessment and its future development on the seasonally frozen ground of the Qinghai-Tibet Plateau under warming-humidifying climate","authors":"","doi":"10.1016/j.coldregions.2024.104293","DOIUrl":"10.1016/j.coldregions.2024.104293","url":null,"abstract":"<div><p>Exploring freeze-thaw landslide susceptibility on the Qinghai-Tibet Plateau (QTP) under warming-humidifying climate is greatly important for preventing and mitigating the risks of landslide hazards on engineering facilities. This study proposed a random forest-based freeze-thaw landslide susceptibility assessment model, where annual rainfall, annual average air temperature (AAAT), slope gradient, normalized difference vegetation index (NDVI), elevation, lithology, and plan curvature were fully considered. Selecting a study area of 324 km² on the seasonally frozen ground (SFG) of QTP with 1059 freeze-thaw landslides, the model accuracy was validated. Low, moderate, high, and very high susceptibility zones were precisely classified, which accounted for 27.0, 27.5, 28.3, and 17.2%, respectively. Furthermore, its future development was explored under warming, humidifying, and warming-humidifying climates. Results indicated that when the AAAT or annual rainfall increased by 1.16 °C or 20 mm, both high and very high susceptibility zones increased by 2.0 or 1.0%, respectively. When AAAT and annual rainfall simultaneously increased by 1.16 °C and 20 mm, a higher increase in the high and very high susceptibility zones of 2.8% occurred. It was noteworthy that climate warming transitioned low and moderate susceptibility zones into high and very high susceptibility zones. These areas where freeze-thaw landslide susceptibility changed featured the AAAT of 4.29–6.15 °C, annual rainfall of 528.9–552.3 mm, slope gradient of 16–25°, and elevation of 3750-3940 m. Compared to climate warming, the humidifying climate and warming-humidifying climate expanded moderate susceptibility zones, and areas where freeze-thaw landslide susceptibility changed featured the gentler slope gradients of 8–16°. This study can provide a better guidance for safe engineering constructions influenced by freeze-thaw landslides on the QTP.</p></div>","PeriodicalId":10522,"journal":{"name":"Cold Regions Science and Technology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142077518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deterioration process and damage constitutive model of concrete under freeze-thaw circumstance of severely cold regions","authors":"","doi":"10.1016/j.coldregions.2024.104290","DOIUrl":"10.1016/j.coldregions.2024.104290","url":null,"abstract":"<div><p>For concrete used in severely cold regions such as the Qinghai-Tibet Plateau, the Northeast China, and the Arctic region, it will inevitably be subjected to the freeze-thaw (FT) cycles close to -40 ℃. However, the lowest temperatures of the conventional concrete FT cycle tests are not lower than -20 ℃. To investigate the differences in concrete damage between the conventional FT cycle circumstance and the severely cold FT cycle circumstance, there are two kinds of the FT cycle test circumstances in this study: -18 ℃ ∼ +5 ℃ (FTC-18) and -40 ℃ ∼ +5 ℃ (FTC-40). The results indicate that, under both FT cycle circumstances, the deterioration rate of concrete escalates as the increase in the FT cycle number. Based on numerical simulation, after the same FT cycle number and under the same stress, the quantity of cracks formed by the load inside the concrete under FTC-40 exceeds that under FTC-18. The results from multi-scale experiments and numerical simulation consistently show that the damage effect of FTC-40 on concrete is more significant than that of FTC-18. The reason for this is that at -40 ℃, more pore water freezes compared to -18 ℃. In addition, the FT damage constitutive models for concrete exposed to both FTC-18 and FTC-40 are developed. The stress-strain curves obtained from the theoretical models exhibit good alignment with the experimental stress-strain curves, thereby confirming the validity and accuracy of the established models.</p></div>","PeriodicalId":10522,"journal":{"name":"Cold Regions Science and Technology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141979403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting frost heave in soil-water systems using the generalized regression neural network optimized with particle swarm optimization algorithm","authors":"","doi":"10.1016/j.coldregions.2024.104291","DOIUrl":"10.1016/j.coldregions.2024.104291","url":null,"abstract":"<div><p>Frost heave poses a serious hazard to geotechnical engineering. However, conventional experimental and theoretical methods, which have limitations in accurately describing the deformation behavior of soils during frost heave, struggle due to the nonlinear and uncertain nature of the process. For this reason, the study leverages the advantages of the Generalized Regression Neural Network (GRNN) in handling nonlinear problems and small sample datasets. The structure of the GRNN model is further optimized using the Particle Swarm Optimization algorithm (PSO) and K-fold Cross Validation (K). The input variables for the model include water content (<em>W</em>), temperature (<em>T</em>), dry density (<em>ρ</em>), and plasticity index (<em>I</em>_<em>p</em>) under various working conditions. The frost heave rate (<em>η</em>) is considered as the output variable. Meanwhile, the model also considers the effects of both one-factor and two-factor interactions among the input variables on frost heave behaviors. Finally, a prediction model for <em>η</em> based on the K-PSO-GRNN is established. The results demonstrate that the K-PSO-GRNN model exhibits greater robustness and stability in predicting <em>η</em> compared to PSO-GRNN and GRNN (R² = 0.94, MAE = 0.14), and the prediction residuals for <em>η</em> range from 0 to 0.4. Among these variables, <em>W</em> has the most significant influence on <em>η</em>, followed by <em>T</em>, <em>ρ</em>, and <em>I</em>_<em>p</em>. Moreover, both <em>ρ</em> and <em>I</em>_<em>p</em> have significant interactions with <em>T</em> and have a notable impact on the soil's frost heave behavior. At high <em>ρ</em>, the soil shows reduced sensitivity to frost heave in response to changes in <em>T</em>, while at high <em>I</em>_<em>p</em>, the soil becomes more sensitive to frost heave with changes in <em>T</em>. <em>η</em> generally shows a positive correlation with <em>W</em> and <em>ρ</em>, and a negative correlation with <em>T</em>. The aforementioned K-PSO-GRNN model can be utilized for predicting <em>η</em>, which is valuable in forecasting non-uniform deformation hazards caused by frost heave and studying preventive measures.</p></div>","PeriodicalId":10522,"journal":{"name":"Cold Regions Science and Technology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141985197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}