Cold Regions Science and Technology最新文献

Superhydrophobic coatings have wide range of engineering applications due to its excellent wettability. However, the durability of superhydrophobic coatings is still challenging under complex environments, especially in cold regions. In this study, we prepared a new self-adaption superhydrophobic cement mortar (SSCM) by wettability modified aggregates with polydimethylsiloxane adhesive and silica nanoparticles decorated by 1H,1H,2H,2H-perfluorodecyltriethoxysilane. Subsequently, a series of multi-scale characteristics of the SSCM sample were systematically investigated, including the micro morphology, the chemical compositions, the self-adaption robustness, the anti−/de-icing properties, and the freeze-thaw (F-T) resistance. The results indicate that surface and inside of the SSCM samples exhibit a self-adaption superhydrophobicity with the 150.0° contact angle due to the low surface energy micro/nano structure of wettability modified aggregates. Meanwhile, the SSCM sample shows excellent anti−/de-icing abilities due to the stable rough micro/nano structure under negative-temperature ambient, which can delay the droplet freezing time by 4.3 times than that of the contrast sample. Additionally, the newly exposed section of SSCM sample can still have the self-adaption superhydrophobicity and endow significant F-T resistance even if the surface of SSCM was damaged. Meanwhile, the SSCM sample can improve the F-T resistance by 45 % compared to the contrast sample. This work provides a new strategy to solve the durability limitation of superhydrophobic coating in engineering practice.

Rock excavation and ore extraction in cold regions (e.g., alpine and high-altitude regions) are often conducted using the drill and blasting method. Ice may be presented in boreholes drilled in cold regions due to the potential freezing of water (e.g., water in rock fractures and pores) flowing into boreholes from the surrounding ground. The performance of rock blasting is inevitably affected by the presence of ice and no study has examined the effect of ice in boreholes on rock blasting performance. The present study investigates rock damage induced by the ice-filling borehole blasting. The damage modes and mechanisms of rock mass under the ice-filling borehole blasting are analyzed. The differences of rock damage induced by the sole ice-filling borehole blasting and the ice-water and ice-air mixed filling borehole blasting are identified. The effects of ice volumes in boreholes on blast-induced rock damage are examined. It is found that blast-induced rock damage is greatly reduced as water in boreholes turns into ice. In addition, the blasting with the ice volume greater than 6.7 % filled at the bottom of borehole can induce less rock damage compared to full-coupled charge blasting. To improve the performance of rock blasting with ice-filling boreholes, the effects of two methods, i.e., changing ignition locations and using different types of explosives on rock damage induced by the ice-filling borehole blasting are investigated. An empirical formula of rock damage volume incorporating ice volume, explosive properties, and rock properties is finally proposed as the reference for the design of ice-filling borehole blasting in cold regions.

The rapid degradation of “Xing'an-Baikal permafrost” in Northeast China has led to various road engineering problems. Efficient inspection and control of pavement quality are critical for maintaining the structural integrity of roads and driving safety in cold regions. Taking the Jagdaqi-Walagan section (JWS) of the Jagdaqi-Mo'he Highway as the object, based on field investigation, unmanned aerial vehicle images and airborne LiDAR data, combined with geographical information system, this study analyzed the pavement damage characteristics in mid- to high-latitude permafrost regions, including quantification of damage ratio, extraction of pavement cracks, and evaluation of pavement roughness and driving quality. The results showed that the average pavement damage ratio was 8.80 %, significantly higher in isolated permafrost regions. A higher damage rate in the Jagdaqi-Mo'he direction than the opposite, with a more concentrated cracking distribution. The worst pavement roughness and most severe pavement bumping at repetitive repair locations. This study provides an effective method for investigating pavement damages and analyzing their mechanisms, and explores the application potential of visible light images combined with LiDAR data in frozen soil engineering. The results provide a scientific basis for assessing current highway conditions, enabling scientific maintenance, and evaluating the risk of engineering damages.

Moraine dams formed by the accumulation of loose glacial materials, typically exhibit poor consolidation properties and complex structure characteristics (especially with buried ice) which make them susceptible to failure. Influenced by global climate warming, the risk of moraine dam breaching is further exacerbated. This article summarizes the breaching modes, breaching mechanisms, and breaching numerical models of moraine dams, and analyzes several deficiencies: 1) The accuracy of long-term monitoring of moraine dams is relatively low, the dynamic monitoring for individual dam and its environment is insufficient; 2) Systematic research on how buried ice affects dam breaching is lacking, and a significant gap remains in the study of combined multiple breaching modes of moraine dams; 3) The predictive results of moraine dam breaching parameter models are quite unreliable, and physically-based mathematical models have not consider the influence of buried ice. Based on this, the article puts forward the following recommendations: 1) Enhance the understanding of climate change impacts on moraine dam breaching through long-term dynamic monitoring of moraine dams; 2) Focus on the erosion characteristics of materials, study the breaching process and mechanisms of ice-rich moraine dams under various breaching modes; 3) Reveal the response mechanism of moraine dams to temperature variations using numerical simulation techniques that couple thermal-stress modules and consider the phase change of ice, further revealing the breaching mechanisms of moraine dams containing buried ice.

Climate warming has affected the transportation infrastructure in Nunavik, Quebec, Canada. Heat drain is an innovative heat extraction technique using density-driven convection of the pore air in the geocomposite of the heat drain to cool the ground during winter. This paper examines the thermal conditions of the road embankment including a heat drain installed in the shoulder at Salluit, Nunavik, Quebec, Canada. Following the installation of the heat drain, a decrease of the soil temperatures was observed. A 2-D finite element geothermal model was developed to reproduce the thermal regime underneath the heat drain, based on the site condition at Salluit. Field measurement of ground temperature for the four year monitoring period from 2012 to 2016, were used to calibrate the model. After the calibration, the long-term climate warming effects on the ground thermal regime was investigated using the model developed.

We have developed the SNOVAL computational code to numerically simulate snow accretion on the conductor wire of a transmission line. Here, we present the theoretical aspects of SNOVAL version 2 such as the derivations of the model equations based on the physical process of snow accretion and conductor wire torsion, and the derivations of the mathematical form of the spatial and temporal discretization of the model equations. The validity of SNOVAL is examined using observational data obtained using a sector model apparatus designed to mimic snow accretion and wire rotation at the center of an actual transmission line. Field observations indicate that the SNOVAL snow accretion model is appropriate, although the SNOVAL results depend strongly on certain computational conditions such as the sticking efficiency, the accreted snow density, and an assumed mass-weighted terminal fall speed of wet snowflakes. Finally, the applicability of SNOVAL to snow accretion on a transmission line is demonstrated via numerical simulation of the dynamic behavior of wire rotation such as the snap-through phenomenon of a conductor wire equipped with counterweights.

Changes in temperature and pressure patterns in gas- and hydrate-saturated permafrost caused by natural geodynamic processes or human impacts can lead to the active flow of gas through unfrozen zones, and its explosive emission is often accompanied by crater formation. Gas flow and accumulation in the shallow permafrost can be explained by the conditions of gas pressure equal to or exceeding the overburden pressure and high-pressure gradients. For the first time, filtration tests were conducted on ice- and hydrate-saturated rocks under uniaxial compression at various negative temperatures using a developed methodology. The modeling of gas flow in a mixture of ice-saturated sand and 25 % montmorillonite at gas pressure gradients within 2 MPa, shows that gas flow can start at warm negative temperatures near the thaw point. Pore hydrate formation in frozen sand heated to positive temperatures and frozen back led to a linear decrease in gas permeability by up to eight times. However, the behavior of gas permeability during hydrate dissociation is nonlinear as it increased within a few hours after the onset of dissociation, but then decreased exponentially in the following 24 h.

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, η, that indicates the relative distribution probability of permafrost at the field scale. PSO-ICA screened and combined 12 high-resolution environmental variables to compose η. 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 η in indicating permafrost. At the field scale, η 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, η 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 η. After downscaling the 1 km resolution SFN to 30 m resolution using η, the R² of the linear relationship between SFN and permafrost temperatures from 43 monitoring boreholes was improved from 0.7010 to 0.8043. If η can help understand the distribution of permafrost at field scale, many engineering and environmental practices could potentially benefit.

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.