Pub Date : 2024-12-28DOI: 10.1016/j.coldregions.2024.104416
Yuhang Liu, Xiangtian Xu, Jiwei Wang, Yongtao Wang, Caixia Fan
As environmental dynamics increasingly influence the stability of engineering materials, accurately predicting sandstone strength under freeze-thaw cycles has become essential. This study presents a new method, IPOA-XGBoost, integrating an improved Pelican Optimization Algorithm (IPOA) with Extreme Gradient Boosting (XGBoost) to accurately forecast sandstone strength in freeze-thaw environments. Principal component analysis (PCA) is employed for reducing data dimensionality, while IPOA optimizes the hyperparameters of the XGBoost model. Experimental findings show that the IPOA-XGBoost model outperforms traditional methods, delivering improved accuracy and robustness in both training and testing datasets. To address the “black box” challenge of machine learning models, SHAP (SHapley Additive exPlanations) values are applied to clarify the impact of individual features on prediction outcomes, validating SHAP's reliability as an interpretive tool. The findings highlight the importance of strain rate (SR), impact pressure (IP), and confining pressure (CP) as key variables affecting sandstone strength predictions. This methodology provides significant insights for predicting sandstone strength in applied engineering contexts.
{"title":"Research on the predictability of rock strength under freeze-thaw cycles - A hybrid model of SHAP-IPOA-XGBoost","authors":"Yuhang Liu, Xiangtian Xu, Jiwei Wang, Yongtao Wang, Caixia Fan","doi":"10.1016/j.coldregions.2024.104416","DOIUrl":"10.1016/j.coldregions.2024.104416","url":null,"abstract":"<div><div>As environmental dynamics increasingly influence the stability of engineering materials, accurately predicting sandstone strength under freeze-thaw cycles has become essential. This study presents a new method, IPOA-XGBoost, integrating an improved Pelican Optimization Algorithm (IPOA) with Extreme Gradient Boosting (XGBoost) to accurately forecast sandstone strength in freeze-thaw environments. Principal component analysis (PCA) is employed for reducing data dimensionality, while IPOA optimizes the hyperparameters of the XGBoost model. Experimental findings show that the IPOA-XGBoost model outperforms traditional methods, delivering improved accuracy and robustness in both training and testing datasets. To address the “black box” challenge of machine learning models, SHAP (SHapley Additive exPlanations) values are applied to clarify the impact of individual features on prediction outcomes, validating SHAP's reliability as an interpretive tool. The findings highlight the importance of strain rate (SR), impact pressure (IP), and confining pressure (CP) as key variables affecting sandstone strength predictions. This methodology provides significant insights for predicting sandstone strength in applied engineering contexts.</div></div>","PeriodicalId":10522,"journal":{"name":"Cold Regions Science and Technology","volume":"231 ","pages":"Article 104416"},"PeriodicalIF":3.8,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154085","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}
Pub Date : 2024-12-27DOI: 10.1016/j.coldregions.2024.104413
K. Roghangar, J.L. Hayley
In permafrost terrain climate change poses a severe threat to infrastructure. Deterministic methods for predicting soil temperature profiles struggle to account for inherent uncertainties in soil properties and surface conditions such as spatial and temporal variations and heterogeneity in surface material characteristics. This paper addresses this limitation by developing a probabilistic thermal analysis model using Monte Carlo simulations in Python, integrated with TEMP/W software. The model provides an estimate of site-scale thaw depth and associated thaw settlement of permafrost sediments in Hudson Bay Railway region under the worst-case climate scenario predictions for 2023–2100. The results of this study indicate that understanding the initial ground temperature conditions is critical for realistic predictions of both short-term and long-term thaw depths and thaw settlement variability. This research reveals that climate warming trends will likely accelerate the rate and depth of permafrost thaw, as evidenced by the increasing variability of possible thaw depth and settlements, which become more diverse and exhibit multiple probabilities as climate warming intensifies throughout the century. The methodology was also used to understand the sensitivity of input parameters and identified moisture content and thawing and freezing indices as the key drivers influencing the magnitude and variability of estimated thaw settlement, respectively. The methodology presented in this study provides valuable information on the distribution of potential outcomes when climate change is incorporated into thaw prediction. This research builds on existing knowledge of uncertainties in permafrost modeling with climate change scenarios and contributes by providing a probabilistic framework that integrates these uncertainties into infrastructure resilience, serviceability, and maintenance assessments.
{"title":"A probabilistic methodology to estimate site-scale thaw settlement in permafrost terrain under climate change","authors":"K. Roghangar, J.L. Hayley","doi":"10.1016/j.coldregions.2024.104413","DOIUrl":"10.1016/j.coldregions.2024.104413","url":null,"abstract":"<div><div>In permafrost terrain climate change poses a severe threat to infrastructure. Deterministic methods for predicting soil temperature profiles struggle to account for inherent uncertainties in soil properties and surface conditions such as spatial and temporal variations and heterogeneity in surface material characteristics. This paper addresses this limitation by developing a probabilistic thermal analysis model using Monte Carlo simulations in Python, integrated with TEMP/W software. The model provides an estimate of site-scale thaw depth and associated thaw settlement of permafrost sediments in Hudson Bay Railway region under the worst-case climate scenario predictions for 2023–2100. The results of this study indicate that understanding the initial ground temperature conditions is critical for realistic predictions of both short-term and long-term thaw depths and thaw settlement variability. This research reveals that climate warming trends will likely accelerate the rate and depth of permafrost thaw, as evidenced by the increasing variability of possible thaw depth and settlements, which become more diverse and exhibit multiple probabilities as climate warming intensifies throughout the century. The methodology was also used to understand the sensitivity of input parameters and identified moisture content and thawing and freezing indices as the key drivers influencing the magnitude and variability of estimated thaw settlement, respectively. The methodology presented in this study provides valuable information on the distribution of potential outcomes when climate change is incorporated into thaw prediction. This research builds on existing knowledge of uncertainties in permafrost modeling with climate change scenarios and contributes by providing a probabilistic framework that integrates these uncertainties into infrastructure resilience, serviceability, and maintenance assessments.</div></div>","PeriodicalId":10522,"journal":{"name":"Cold Regions Science and Technology","volume":"231 ","pages":"Article 104413"},"PeriodicalIF":3.8,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155030","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}
Pub Date : 2024-12-26DOI: 10.1016/j.coldregions.2024.104414
Binqiang Li , Jun Luo , Yanbo Bai , Zhenxing He , Yapeng Wang , Penghao Li , Wanming Zhai
Uneven frost heave is frequently encountered in the subgrade-bridge transition zones (SBTZ) in seasonally frozen soil regions, which could lead to the deformation of track and even jeopardize running safety of vehicles. To this end, this paper conducts dynamic analysis of a vehicle-track coupled system accounting for the effect of frost heave deformation. Initially, the finite element method is used to obtain the relationship between rail irregularity and frost heave deformation. Then, a vehicle-track vertically coupled dynamics model is established, and its accuracy is validated by the measured data, published results and existing model. The time-domain dynamic responses of a vehicle-track coupled system under typical frost heave are analyzed. Afterwards, parametric analysis of frost heave deformation is conducted. Finally, the control threshold of frost heave is proposed from aspects of vehicle running safety, comfort, and track deformation. Numerical results indicate that the allowable amplitude of frost heave should be respectively restricted to 5, 20, and 25 mm for frost heave wavelengths less than 10 m, between 10 and 15 m, and greater than 15 m. The research findings offer theoretical support for the maintenance and operation of track in the SBTZ in seasonally frozen soil regions.
{"title":"Dynamic analysis of a vehicle-track coupled system subjected to uneven frost heave in the subgrade-bridge transition zone","authors":"Binqiang Li , Jun Luo , Yanbo Bai , Zhenxing He , Yapeng Wang , Penghao Li , Wanming Zhai","doi":"10.1016/j.coldregions.2024.104414","DOIUrl":"10.1016/j.coldregions.2024.104414","url":null,"abstract":"<div><div>Uneven frost heave is frequently encountered in the subgrade-bridge transition zones (SBTZ) in seasonally frozen soil regions, which could lead to the deformation of track and even jeopardize running safety of vehicles. To this end, this paper conducts dynamic analysis of a vehicle-track coupled system accounting for the effect of frost heave deformation. Initially, the finite element method is used to obtain the relationship between rail irregularity and frost heave deformation. Then, a vehicle-track vertically coupled dynamics model is established, and its accuracy is validated by the measured data, published results and existing model. The time-domain dynamic responses of a vehicle-track coupled system under typical frost heave are analyzed. Afterwards, parametric analysis of frost heave deformation is conducted. Finally, the control threshold of frost heave is proposed from aspects of vehicle running safety, comfort, and track deformation. Numerical results indicate that the allowable amplitude of frost heave should be respectively restricted to 5, 20, and 25 mm for frost heave wavelengths less than 10 m, between 10 and 15 m, and greater than 15 m. The research findings offer theoretical support for the maintenance and operation of track in the SBTZ in seasonally frozen soil regions.</div></div>","PeriodicalId":10522,"journal":{"name":"Cold Regions Science and Technology","volume":"231 ","pages":"Article 104414"},"PeriodicalIF":3.8,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155352","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}
Pub Date : 2024-12-25DOI: 10.1016/j.coldregions.2024.104412
James Dillon , Erich Peitzsch , Zachary Miller , Perry Bartelt , Kevin Hammonds
Glide avalanches present a significant and repetitive challenge to many operational forecasting programs, and they are likely to become more frequent. While the spatial location of glide release areas is extremely consistent, the onset of glide avalanche release is notoriously difficult to forecast, and their destructive potential can be immense. Thus, the timing and dynamics of glide avalanches is an important area of study. To better understand these processes, and to improve assessments of risk to transportation corridors and infrastructure, event documentation is key. Here, we survey a large glide avalanche event along the Going-to-the-Sun Road in Glacier National Park, Montana, USA, during road opening operations in the spring of 2022. Using three sets of terrestrial lidar data (pre-event, post-event, and snow-off), we quantified key aspects of the avalanche and created powerful visualizations for analysis. Further, we evaluated meteorological data from automated weather stations between the onset of glide cracking and avalanche release. Last, we synthesized lidar data with a numerical dynamics model to replicate the event in a simulated environment. Using the tuned model, we determined the critical mean snow depth in the release area necessary for an avalanche to reach the road (4.2 m). Our method may be of particular use for glide avalanches, which tend to release in roughly the same place and time each year at a known interface. This could make the calculated critical depths more consistently reliable and preclude the need for additional tuning in dynamics models. As 1) lidar technology continues to improve and reduce in cost, 2) transportation corridors continue to extend into avalanche terrain, and 3) glide avalanches potentially become increasingly frequent, the synthesis outlined here provides a valuable tool for operational forecasters considering infrastructure threatened by glide events.
{"title":"Documenting, quantifying, and modeling a large glide avalanche in Glacier National Park, Montana, USA","authors":"James Dillon , Erich Peitzsch , Zachary Miller , Perry Bartelt , Kevin Hammonds","doi":"10.1016/j.coldregions.2024.104412","DOIUrl":"10.1016/j.coldregions.2024.104412","url":null,"abstract":"<div><div>Glide avalanches present a significant and repetitive challenge to many operational forecasting programs, and they are likely to become more frequent. While the spatial location of glide release areas is extremely consistent, the onset of glide avalanche release is notoriously difficult to forecast, and their destructive potential can be immense. Thus, the timing and dynamics of glide avalanches is an important area of study. To better understand these processes, and to improve assessments of risk to transportation corridors and infrastructure, event documentation is key. Here, we survey a large glide avalanche event along the Going-to-the-Sun Road in Glacier National Park, Montana, USA, during road opening operations in the spring of 2022. Using three sets of terrestrial lidar data (pre-event, post-event, and snow-off), we quantified key aspects of the avalanche and created powerful visualizations for analysis. Further, we evaluated meteorological data from automated weather stations between the onset of glide cracking and avalanche release. Last, we synthesized lidar data with a numerical dynamics model to replicate the event in a simulated environment. Using the tuned model, we determined the critical mean snow depth in the release area necessary for an avalanche to reach the road (4.2 m). Our method may be of particular use for glide avalanches, which tend to release in roughly the same place and time each year at a known interface. This could make the calculated critical depths more consistently reliable and preclude the need for additional tuning in dynamics models. As 1) lidar technology continues to improve and reduce in cost, 2) transportation corridors continue to extend into avalanche terrain, and 3) glide avalanches potentially become increasingly frequent, the synthesis outlined here provides a valuable tool for operational forecasters considering infrastructure threatened by glide events.</div></div>","PeriodicalId":10522,"journal":{"name":"Cold Regions Science and Technology","volume":"231 ","pages":"Article 104412"},"PeriodicalIF":3.8,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154069","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}
Frost heave susceptibility (FHS) is a criterion to quantify the potential of soil to produce frost heave, which is widely applied in infrastructure construction in cold regions. Although more than 100 methods have been proposed to assess FHS, there are large discrepancies between the predicted and observed FHS, and their performance has not been systematically compared. Hence, this study summarizes the definitions, classifications, testing methods, advantages, and disadvantages of seven commonly used methods based on the following indices, i.e., fine content (USACE, 1984), segregation potential SP (Konrad, 1980), the R index (Teng et al., 2023), frost heave rate (ASTM D5918, 2013), frost heave ratio (GB-50324, 2014), frost heave (Roe and Webster, 1984), and frost heave slope p (NF P98–234-2, 1996). Comparative analysis of these methods shows that each method has its own applicable conditions. The key is to choose an appropriate method to assess FHS according to the specific conditions. Frost heave rate, frost heave, frost heave slope p, and the R are suitable for evaluating macroscopic frost heave. Segregation potential effectively describes the water flow velocity at the frozen fringe. Frost heave ratio characterizes both frost heave and frost depth. At the engineering scale, the fine content method offers significant advantages. Further analyses indicate that the R and p indices are the stable methods. Frost heave and frost heave ratio increase over time, while segregation potential and frost heave rate generally exhibit a decreasing trend. Methods with similar dimensions exhibit strong linear relationships between them, while those with different dimensions lack substantial relationships, making the comparison challenging. This paper may provide a reference for selecting a method to assess FHS.
{"title":"Comparative evaluation of the methods of assessing frost heave susceptibility","authors":"Haosong He, Jidong Teng, Shengwei Zhao, Wei Guan, Sheng Zhang","doi":"10.1016/j.coldregions.2024.104406","DOIUrl":"10.1016/j.coldregions.2024.104406","url":null,"abstract":"<div><div>Frost heave susceptibility (FHS) is a criterion to quantify the potential of soil to produce frost heave, which is widely applied in infrastructure construction in cold regions. Although more than 100 methods have been proposed to assess FHS, there are large discrepancies between the predicted and observed FHS, and their performance has not been systematically compared. Hence, this study summarizes the definitions, classifications, testing methods, advantages, and disadvantages of seven commonly used methods based on the following indices, i.e., fine content (<span><span>USACE, 1984</span></span>), segregation potential SP (<span><span>Konrad, 1980</span></span>), the <em>R</em> index (<span><span>Teng et al., 2023</span></span>), frost heave rate (<span><span>ASTM D5918, 2013</span></span>), frost heave ratio (<span><span>GB-50324, 2014</span></span>), frost heave (<span><span>Roe and Webster, 1984</span></span>), and frost heave slope <em>p</em> (<span><span>NF P98–234-2, 1996</span></span>). Comparative analysis of these methods shows that each method has its own applicable conditions. The key is to choose an appropriate method to assess FHS according to the specific conditions. Frost heave rate, frost heave, frost heave slope <em>p</em>, and the <em>R</em> are suitable for evaluating macroscopic frost heave. Segregation potential effectively describes the water flow velocity at the frozen fringe. Frost heave ratio characterizes both frost heave and frost depth. At the engineering scale, the fine content method offers significant advantages. Further analyses indicate that the <em>R</em> and <em>p</em> indices are the stable methods. Frost heave and frost heave ratio increase over time, while segregation potential and frost heave rate generally exhibit a decreasing trend. Methods with similar dimensions exhibit strong linear relationships between them, while those with different dimensions lack substantial relationships, making the comparison challenging. This paper may provide a reference for selecting a method to assess FHS.</div></div>","PeriodicalId":10522,"journal":{"name":"Cold Regions Science and Technology","volume":"231 ","pages":"Article 104406"},"PeriodicalIF":3.8,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155034","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}
Natural disasters like avalanches and earthquakes are examples of rare events. Predicting such events using supervised classification machine learning models suffers from the class imbalance problem. The number of non-avalanche days exceed the number of avalanche days, and such data distribution skewness interferes with the construction of decision boundaries to support the decision-making procedure. This paper analyses class imbalance from the perspective of avalanche prediction by involving multiple classification approaches, three oversampling and two undersampling techniques, and cost-sensitive approaches. The supervised approaches aimed to predict days with and without avalanches as binary classification. The study was conducted using past 25 seasons of snow and meteorological parameters recorded for two climatologically diverse avalanche prone regions of Indian Himalayas with different levels of class imbalance. The paper also proposes more extensive use of evaluation metrics like balanced accuracy, geometric mean, Probability of Detection (POD) and Peirce Skill Score (PSS) that are pertinent to imbalanced class domains like avalanche forecasting. Extensive empirical experiments and evaluations amply demonstrate that these class balancing techniques lead to significant improvements in the performance of avalanche forecasting models for both regions, albeit with some variations. The POD values improved to 0.83 for Random Forest classifier, 0.65 for Support Vector Machine classifier and 0.75 for Logistic Regression classifier; PSS values also improved to 0.53, 0.47 and 0.5 for Random Forest, Support Vector Machine, and Logistic Regression classifiers, respectively. These findings are complemented by theoretical insights on the proposed solutions to the class imbalance. Our results suggest that the classification based avalanche forecasting models trained using proposed approaches can serve as valuable supplementary decision support tool for avalanche forecasters.
{"title":"Addressing class imbalance in avalanche forecasting","authors":"Manish Kala , Shweta Jain , Amreek Singh , Narayanan Chatapuram Krishnan","doi":"10.1016/j.coldregions.2024.104411","DOIUrl":"10.1016/j.coldregions.2024.104411","url":null,"abstract":"<div><div>Natural disasters like avalanches and earthquakes are examples of rare events. Predicting such events using supervised classification machine learning models suffers from the class imbalance problem. The number of non-avalanche days exceed the number of avalanche days, and such data distribution skewness interferes with the construction of decision boundaries to support the decision-making procedure. This paper analyses class imbalance from the perspective of avalanche prediction by involving multiple classification approaches, three oversampling and two undersampling techniques, and cost-sensitive approaches. The supervised approaches aimed to predict days with and without avalanches as binary classification. The study was conducted using past 25 seasons of snow and meteorological parameters recorded for two climatologically diverse avalanche prone regions of Indian Himalayas with different levels of class imbalance. The paper also proposes more extensive use of evaluation metrics like balanced accuracy, geometric mean, Probability of Detection (POD) and Peirce Skill Score (PSS) that are pertinent to imbalanced class domains like avalanche forecasting. Extensive empirical experiments and evaluations amply demonstrate that these class balancing techniques lead to significant improvements in the performance of avalanche forecasting models for both regions, albeit with some variations. The POD values improved to 0.83 for Random Forest classifier, 0.65 for Support Vector Machine classifier and 0.75 for Logistic Regression classifier; PSS values also improved to 0.53, 0.47 and 0.5 for Random Forest, Support Vector Machine, and Logistic Regression classifiers, respectively. These findings are complemented by theoretical insights on the proposed solutions to the class imbalance. Our results suggest that the classification based avalanche forecasting models trained using proposed approaches can serve as valuable supplementary decision support tool for avalanche forecasters.</div></div>","PeriodicalId":10522,"journal":{"name":"Cold Regions Science and Technology","volume":"231 ","pages":"Article 104411"},"PeriodicalIF":3.8,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155033","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}
Pub Date : 2024-12-23DOI: 10.1016/j.coldregions.2024.104410
Ilija Samardžija , Knut V. Høyland , Bernt J. Leira , Arvid Naess
As defined by the international standard ISO 19906, the representative ice actions should be estimated for ELIE (extreme-level ice event) and ALIE (abnormal-level ice event). These events are defined by their relevant annual exceedance probability levels. Probabilistic methods are often used to obtain a proper characterization of the ice actions from which representative ice actions can be inferred. In this paper, we consider ice actions caused by first-year ice ridges. Ridge action depends on a large number of input parameters. Important correlations between the parameters add to the complexity of ridge action and need to be included in probabilistic assessments to obtain reliable results. Data for establishing input probability distributions are often incomplete, biased, or completely non-existent. To solve this problem, it is common engineering practice to combine data from locations other than the location of interest and make ad hoc extrapolations and assumptions. This often leads to overly conservative estimates of representative ice ridge actions. We propose a framework for Monte Carlo simulation of the first-year ice ridge actions. The goal is to establish a method for which the need of input data is at a minimum. The only needed input data in our simulation is the statistics of the annual maximum level ice thickness and statistics related to ice being present or not. Based on correlations and findings from our previous studies, we are able to simulate other input parameters such as ridge keel draft, ridge frequency and consolidated layer thickness. In this paper, we also discuss the problem of defining the probability distribution for the ice strength coefficient CR in the context of Monte Carlo simulations. Our approach is dependent on our previous research that is based on data from the Beaufort Sea. Without an appropriate calibration of the correlations between the parameters, we cannot be certain if the simulation can be extended to other locations by simply adjusting the input maximum annual level ice statistics. Nevertheless, we believe that our approach can be used as a tool for preliminary probabilistic assessment of ridge action. Our approach offers good flexibility, and we believe that with suitable data it can be calibrated for other geographical locations and structure types.
{"title":"Probabilistic assessment of first-year ice ridge action on offshore structures","authors":"Ilija Samardžija , Knut V. Høyland , Bernt J. Leira , Arvid Naess","doi":"10.1016/j.coldregions.2024.104410","DOIUrl":"10.1016/j.coldregions.2024.104410","url":null,"abstract":"<div><div>As defined by the international standard ISO 19906, the representative ice actions should be estimated for ELIE (extreme-level ice event) and ALIE (abnormal-level ice event). These events are defined by their relevant annual exceedance probability levels. Probabilistic methods are often used to obtain a proper characterization of the ice actions from which representative ice actions can be inferred. In this paper, we consider ice actions caused by first-year ice ridges. Ridge action depends on a large number of input parameters. Important correlations between the parameters add to the complexity of ridge action and need to be included in probabilistic assessments to obtain reliable results. Data for establishing input probability distributions are often incomplete, biased, or completely non-existent. To solve this problem, it is common engineering practice to combine data from locations other than the location of interest and make ad hoc extrapolations and assumptions. This often leads to overly conservative estimates of representative ice ridge actions. We propose a framework for Monte Carlo simulation of the first-year ice ridge actions. The goal is to establish a method for which the need of input data is at a minimum. The only needed input data in our simulation is the statistics of the annual maximum level ice thickness and statistics related to ice being present or not. Based on correlations and findings from our previous studies, we are able to simulate other input parameters such as ridge keel draft, ridge frequency and consolidated layer thickness. In this paper, we also discuss the problem of defining the probability distribution for the ice strength coefficient C<sub>R</sub> in the context of Monte Carlo simulations. Our approach is dependent on our previous research that is based on data from the Beaufort Sea. Without an appropriate calibration of the correlations between the parameters, we cannot be certain if the simulation can be extended to other locations by simply adjusting the input maximum annual level ice statistics. Nevertheless, we believe that our approach can be used as a tool for preliminary probabilistic assessment of ridge action. Our approach offers good flexibility, and we believe that with suitable data it can be calibrated for other geographical locations and structure types.</div></div>","PeriodicalId":10522,"journal":{"name":"Cold Regions Science and Technology","volume":"231 ","pages":"Article 104410"},"PeriodicalIF":3.8,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154068","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}
Pub Date : 2024-12-21DOI: 10.1016/j.coldregions.2024.104408
Yi Zhou , Yu Zhang , Changsheng Chen , Lele Li , Danya Xu , Robert C. Beardsley , Weizeng Shao
The accuracy of Arctic sea ice thickness retrieved from the CryoSat-2 satellite is significantly influenced by the sea ice surface roughness, snow backscatter, and snow depth. In this study, four updated cases incorporating physical model-based radar freeboard, newly estimated radar penetration rate, and well-validated satellite snow depth were constructed to evaluate their potential improvements to the Alfred Wegener Institute's CryoSat-2 sea ice thickness (AWI CS2). The updated cases were then compared with airborne remotely sensed observations from the National Aeronautics and Space Administration's Operation IceBridge (OIB) and CryoSat Validation Experiment (CryoVEx) in 2013 and 2014, as well as with ground-based observations during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to April 2020. The results showed that all updated cases had the potential to improve the accuracy of sea ice thickness, maintaining comparable correlation coefficients and significantly reducing statistical errors compared to the AWI CS2. In the evaluation with OIB, CryoVEx, and MOSAiC, the four updated cases reduced the root mean square error of AWI CS2 by up to 0.68 m (55 %) against OIB, 0.76 m (53 %) against CryoVEx, and 0.47 m (76 %) against MOSAiC. The updated sea ice thicknesses retained the main distribution patterns generated by AWI CS2, but generally showed thinner sea ice thicknesses. From 2013 to 2018, the interannual variation trends between the updated cases and AWI CS2 varied regionally, but both show significant decreasing trends along the northern coasts of the Canadian Arctic Archipelago and Greenland. The updated schemes provided new insights into the retrieval of sea ice thickness using CryoSat-2, thereby further contributing to the quantification of the sea ice volume in the context of a warming climate.
{"title":"Assessment of radar freeboard, radar penetration rate, and snow depth for potential improvements in Arctic sea ice thickness retrieved from CryoSat-2","authors":"Yi Zhou , Yu Zhang , Changsheng Chen , Lele Li , Danya Xu , Robert C. Beardsley , Weizeng Shao","doi":"10.1016/j.coldregions.2024.104408","DOIUrl":"10.1016/j.coldregions.2024.104408","url":null,"abstract":"<div><div>The accuracy of Arctic sea ice thickness retrieved from the CryoSat-2 satellite is significantly influenced by the sea ice surface roughness, snow backscatter, and snow depth. In this study, four updated cases incorporating physical model-based radar freeboard, newly estimated radar penetration rate, and well-validated satellite snow depth were constructed to evaluate their potential improvements to the Alfred Wegener Institute's CryoSat-2 sea ice thickness (AWI CS2). The updated cases were then compared with airborne remotely sensed observations from the National Aeronautics and Space Administration's Operation IceBridge (OIB) and CryoSat Validation Experiment (CryoVEx) in 2013 and 2014, as well as with ground-based observations during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to April 2020. The results showed that all updated cases had the potential to improve the accuracy of sea ice thickness, maintaining comparable correlation coefficients and significantly reducing statistical errors compared to the AWI CS2. In the evaluation with OIB, CryoVEx, and MOSAiC, the four updated cases reduced the root mean square error of AWI CS2 by up to 0.68 m (55 %) against OIB, 0.76 m (53 %) against CryoVEx, and 0.47 m (76 %) against MOSAiC. The updated sea ice thicknesses retained the main distribution patterns generated by AWI CS2, but generally showed thinner sea ice thicknesses. From 2013 to 2018, the interannual variation trends between the updated cases and AWI CS2 varied regionally, but both show significant decreasing trends along the northern coasts of the Canadian Arctic Archipelago and Greenland. The updated schemes provided new insights into the retrieval of sea ice thickness using CryoSat-2, thereby further contributing to the quantification of the sea ice volume in the context of a warming climate.</div></div>","PeriodicalId":10522,"journal":{"name":"Cold Regions Science and Technology","volume":"231 ","pages":"Article 104408"},"PeriodicalIF":3.8,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155348","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}
Pub Date : 2024-12-19DOI: 10.1016/j.coldregions.2024.104405
Qijun Xie , Fulai Zhang , Lijun Su
The frozen moraine soil is geographically distributed across the Qinghai-Tibet Plateau and its surrounding areas, serving as a fundamental substrate for engineering projects such as the Sichuan-Tibet Railway and the China-Pakistan Highway. As an economical and efficient construction technique, blasting is a commonly employed in these projects. Understanding the dynamic mechanical response, damage, and failure characteristics of moraine soil is crucial for accurately predicting the impact of blasting. Therefore, this study utilizes the Split Hopkinson Pressure Bar (SHPB) equipment to conduct impact tests on moraine soil under different temperatures and strain rates. Additionally, a model for predicting the dynamic mechanical response of frozen moraine soil has been proposed based on peridynamic theory, decohesion damage theory, and the ZWT model, in which the debonding damage and the adiabatic temperature rise are considered. This model focuses on considering the bonds between different substances within frozen moraine soil. By defining the mechanical response of these bonds, the impact deformation mechanism of frozen moraine soil is unveiled. Within this, the modeling of ice-cemented bonds contributes to a deeper understanding of the crack propagation characteristics in frozen moraine soil. The model prediction results demonstrate its capability to predict various aspects of the dynamic response of frozen moraine under impact loading, including the macroscopic stress-strain behavior, the mesoscopic crack initiation and propagation, and the influence of adiabatic temperature rise on the damage mechanism, as well as evaluate the damage state of frozen moraine soil under impact loading.
{"title":"A model for predicting the mechanical properties of frozen moraine soil under impact loading","authors":"Qijun Xie , Fulai Zhang , Lijun Su","doi":"10.1016/j.coldregions.2024.104405","DOIUrl":"10.1016/j.coldregions.2024.104405","url":null,"abstract":"<div><div>The frozen moraine soil is geographically distributed across the Qinghai-Tibet Plateau and its surrounding areas, serving as a fundamental substrate for engineering projects such as the Sichuan-Tibet Railway and the China-Pakistan Highway. As an economical and efficient construction technique, blasting is a commonly employed in these projects. Understanding the dynamic mechanical response, damage, and failure characteristics of moraine soil is crucial for accurately predicting the impact of blasting. Therefore, this study utilizes the Split Hopkinson Pressure Bar (SHPB) equipment to conduct impact tests on moraine soil under different temperatures and strain rates. Additionally, a model for predicting the dynamic mechanical response of frozen moraine soil has been proposed based on peridynamic theory, decohesion damage theory, and the ZWT model, in which the debonding damage and the adiabatic temperature rise are considered. This model focuses on considering the bonds between different substances within frozen moraine soil. By defining the mechanical response of these bonds, the impact deformation mechanism of frozen moraine soil is unveiled. Within this, the modeling of ice-cemented bonds contributes to a deeper understanding of the crack propagation characteristics in frozen moraine soil. The model prediction results demonstrate its capability to predict various aspects of the dynamic response of frozen moraine under impact loading, including the macroscopic stress-strain behavior, the mesoscopic crack initiation and propagation, and the influence of adiabatic temperature rise on the damage mechanism, as well as evaluate the damage state of frozen moraine soil under impact loading.</div></div>","PeriodicalId":10522,"journal":{"name":"Cold Regions Science and Technology","volume":"231 ","pages":"Article 104405"},"PeriodicalIF":3.8,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155350","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}
Pub Date : 2024-12-17DOI: 10.1016/j.coldregions.2024.104400
Emmanuel Quayson-Sackey , Baafour Nyantekyi-Kwakye , Godwin K. Ayetor
Due to the abundance of wind resources in marine environments, offshore wind turbines (OWTs) have gained significant attention in recent years. However, their blades are prone to ice accretion when operating in cold climates. Ice accretion on OWT blades induces surface roughness thereby reducing the aerodynamic performance of the turbine. Although various ice mitigation techniques have been explored, tested, and applied to onshore wind turbines, their feasibility for offshore application remains uncertain. Therefore, this review conducts a comprehensive feasibility study, examining each ice mitigation technique, its fundamental principles, advantages, disadvantages, and the potential for successful integration on OWT blades. The study also highlights the challenges of implementing these techniques in harsh offshore environments, providing critical insights for future research in this field.
{"title":"Technological advancements for anti-icing and de-icing offshore wind turbine blades","authors":"Emmanuel Quayson-Sackey , Baafour Nyantekyi-Kwakye , Godwin K. Ayetor","doi":"10.1016/j.coldregions.2024.104400","DOIUrl":"10.1016/j.coldregions.2024.104400","url":null,"abstract":"<div><div>Due to the abundance of wind resources in marine environments, offshore wind turbines (OWTs) have gained significant attention in recent years. However, their blades are prone to ice accretion when operating in cold climates. Ice accretion on OWT blades induces surface roughness thereby reducing the aerodynamic performance of the turbine. Although various ice mitigation techniques have been explored, tested, and applied to onshore wind turbines, their feasibility for offshore application remains uncertain. Therefore, this review conducts a comprehensive feasibility study, examining each ice mitigation technique, its fundamental principles, advantages, disadvantages, and the potential for successful integration on OWT blades. The study also highlights the challenges of implementing these techniques in harsh offshore environments, providing critical insights for future research in this field.</div></div>","PeriodicalId":10522,"journal":{"name":"Cold Regions Science and Technology","volume":"231 ","pages":"Article 104400"},"PeriodicalIF":3.8,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154385","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}
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