Cold Regions Science and Technology最新文献

{"title":"An indicator of relative distribution probability of field-scale permafrost in Northeast China: Using a particle swarm optimization (PSO)-based indicator composition algorithm","authors":"","doi":"10.1016/j.coldregions.2024.104311","DOIUrl":"10.1016/j.coldregions.2024.104311","url":null,"abstract":"<div><p>Under the influence of climate changing, permafrost in Northeast China (NEC) has been consistently degrading in recent years. Numerous scholars have investigated the spatial and temporal distribution patterns of permafrost in the NEC region. However, due to constraints in data availability and methodological approaches, only a limited number of studies have extended their analyses to the field scale. In this study, we established a particle swarm optimization (PSO)-based indicator composition algorithm (PSO-ICA) to obtain an indicator factor, <em>η</em>, that indicates the relative distribution probability of permafrost at the field scale. PSO-ICA screened and combined 12 high-resolution environmental variables to compose <em>η</em>. The spatial distribution data of permafrost with a length of 765.378 km provided by the engineering geological investigation report (EGIR) of six highways were used to train and validate the effectiveness of <em>η</em> in indicating permafrost. At the field scale, <em>η</em> was found to be similar to the surface freezing number (SFN) in its ability to indicate permafrost, with AUC values of 0.7046 and 0.7063 for the two by the ROC test. In addition, <em>η</em> has a good performance in predicting highway distresses in the permafrost region in the absence of survey data. This study also confirmed that the resolution and accuracy of permafrost mapping results can be improved by utilizing <em>η</em>. After downscaling the 1 km resolution SFN to 30 m resolution using <em>η</em>, the R² of the linear relationship between SFN and permafrost temperatures from 43 monitoring boreholes was improved from 0.7010 to 0.8043. If <em>η</em> can help understand the distribution of permafrost at field scale, many engineering and environmental practices could potentially benefit.</p></div>","PeriodicalId":10522,"journal":{"name":"Cold Regions Science and Technology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142157905","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 comprehensive review of AI-based methods used for forecasting ice jam floods occurrence, severity, timing, and location","authors":"","doi":"10.1016/j.coldregions.2024.104305","DOIUrl":"10.1016/j.coldregions.2024.104305","url":null,"abstract":"<div><p>River ice breakup can affect most rivers in cold climate during winter, posing a serious threat of Ice-Jam Floods (IJFs) to riverine communities. IJFs are challenging to predict due to their chaotic nature that arises from the complex interaction between hydroclimatic factors and river morphology. In addition, climate change has significantly impacted river ice patterns and the severity of IJFs in recent decades. However, recent advancements in computing power have led to the development of several Artificial Intelligence (AI) approaches to forecast IJF. Still, there is a lack of a systematic review that can adequately compare the different AI approaches together with the different hydrometeorological parameters used to forecast IJF. Therefore, the primary objective of this study is to review the various existing AI-based IJFs prediction models, their input parameters, and their potential strengths and limitations. The review showed that AI-based IJF prediction models can be grouped into four categories based on their objectives to forecast IJF occurrence, severity, timing, and location. The study also revealed that station-based data remained the primary source of information for predicting IJFs, but there has been a growing trend in recent years toward remote sensing, reanalysis products, and national databases, indicating their increasing prominence. Overall, air temperature, precipitation, and hydrometric parameters (discharge and water level) were the most frequently utilized input parameters. The review also categorized AI-based IJF forecasting models into four types: machine learning, hybrid, ensemble, and framework models. Although the framework approach has gained recent popularity in recent years, but still the machine learning and ensemble models were the most frequently used. While directly comparing the capabilities and limitations of different modeling approaches without considering the specific context of the sites in which they were applied can be misleading, several studies have demonstrated the potential of ensemble and hybrid approaches to improve model accuracy compared to single machine learning models. However, more studies are needed to confirm these conclusions.</p></div>","PeriodicalId":10522,"journal":{"name":"Cold Regions Science and Technology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0165232X24001861/pdfft?md5=1d8780bbb4f6318d2b81ab84d8f4cfdc&pid=1-s2.0-S0165232X24001861-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142136077","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":"Prediction model for icing growth characteristics of high-speed railway contact lines","authors":"","doi":"10.1016/j.coldregions.2024.104306","DOIUrl":"10.1016/j.coldregions.2024.104306","url":null,"abstract":"<div><p>The sliding electrical contact is the only means by which high-speed trains obtain energy. When icing occurs on the contact lines, the impact vibrations of the pantograph-catenary system are further exacerbated, electrical arcing becomes more frequent, and abnormal wear is caused, seriously threatening the safety of the energy supply for high-speed railways. To address the unclear mechanisms, unpredictable patterns, and challenging characterization of contact lines icing, this paper proposes a dynamic simulation method for the first time. Furthermore, a surrogate model for predicting contact line icing is developed using deep learning algorithms. First, based on grid updating, flow field analysis, and icing calculations, key icing parameters are obtained to establish a numerical model of contact lines icing under time-varying meteorological parameters. Then, the effects of factors such as wind speed, temperature, and liquid water content on the dynamic evolution characteristics of contact line icing are analyzed. Finally, using the CNN-GRU algorithm, a prediction model for contact line icing is constructed to predict the icing mass and contours. This research clarifies the evolution patterns of contact lines icing, addresses challenges in monitoring and predicting icing states, and lays a theoretical foundation for high-speed railways' safe and stable operation under icing conditions.</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":"142136076","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":"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}