Slope failures resulting from thaw slumps in permafrost regions, have developed widely under the influence of climate change and engineering activities. The shear strength at the interface between the active layer and permafrost (IBALP) at maximum thawing depth is a critical factor to evaluate stability of permafrost slopes. Traditional direct shear, triaxial shear, and large-scale in-situ shear experiments are unsuitable for measuring the shear strength parameter of the IBALP. Based on the characteristics of thaw slumps in permafrost regions, this study proposes a novel test method of self-weight direct shear instrument (SWDSI), and its principle, structure, measurement system and test steps are described in detail. The shear strength of the IBALP under maximum thaw depth conditions is measured using this method. The results show that under the condition that the permafrost layer is thick underground ice and the active layer consists of silty clay with 20% water content, the test results are in good agreement with the results of field large-scale direct shear tests and are in accordance with previous understandings and natural laws. The above analysis indicates that the method of the SWDSI has a reliable theoretical basis and reasonable experimental procedures, and meets the needs of stability assessment of thaw slumps in permafrost regions. The experimental data obtained provide important parameter support for the evaluation of related geological hazards.

This study assesses the stability of the Bei'an-Hei'he Highway (BHH), located near the southern limit of latitudinal permafrost in the Xiao Xing'anling Mountains, Northeast China, where permafrost degradation is intensifying under combined climatic and anthropogenic influences. Freeze-thaw-induced ground deformation and related periglacial hazards remain poorly quantified, limiting regional infrastructure resilience. We developed an integrated framework that fuses multi-source InSAR (ALOS, Sentinel-1, ALOS-2), unmanned aerial vehicle (UAV) photogrammetry, electrical resistivity tomography (ERT), and theoretical modeling to characterize cumulative deformation, evaluate present stability, and project future dynamics. Results reveal long-term deformation rates from -35 to +40 mm/yr within a 1-km buffer on each side of the BHH, with seasonal amplitudes up to 11 mm. Sentinel-1, with its 12-day revisit cycle, demonstrated superior capability for monitoring the Xing'an permafrost. Deformation patterns were primarily controlled by air temperature, while precipitation and the topographic wetness index enhanced spatial heterogeneity through thermo-hydrological coupling. Wavelet analysis identified a 334-day deformation cycle, lagging climate forcing by similar to 107 days due to the insulating effects of peat. Early-warning analysis classified 4.99 % of the highway length as high-risk (subsidence 10.91 mm/yr). The InSAR-based landslide prediction model achieved high accuracy (Area Under the Receiver Operating Characteristic (ROC) Curve, or AUC = 0.9486), validated through field surveys of subsidence, cracking, and slow-moving failures. The proposed 'past-present-future' framework demonstrates the potential of multi-sensor integration for permafrost monitoring and provides a transferable approach for assessing infrastructure stability in cold regions.

Infrastructure in northern regions is increasingly threatened by climate change, mainly due to permafrost thaw. Prediction of permafrost stability is essential for assessing the long-term stability of such infrastructure. A key aspect of geotechnical problems subject to climate change is addressing the surface energy balance (SEB). In this study, we evaluated three methodologies for applying surface boundary conditions in longterm thermal geotechnical analyses, including SEB heat flux, n-factors, and machine learning (ML) models by using ERA5-Land climate reanalysis data until 2100. We aimed to determine the most effective approach for accurately predicting ground surface temperatures for climate-resilient design of northern infrastructure. The evaluation results indicated that the ML-based approach outperformed both the SEB heat flux and n-factors methods, demonstrating significantly lower prediction errors. The feasibility of long-term thermal analysis of geotechnical problems using ML-predicted ground surface temperatures was then demonstrated through a permafrost case study in the community of Salluit in northern Canada, for which the thickness of the active layer and talik were calculated under moderate and extreme climate scenarios by the end of the 21st century. Finally, we discussed the application and limitations of surface boundary condition methodologies, such as the limited applicability of the n-factors in long-term analysis and the sensitivity of the SEB heat flux to inputs and thermal imbalance. The findings highlight the importance of selecting suitable boundary condition methodologies in enhancing the reliability of thermal geotechnical analyses in cold regions.

Here, we present the result of different models for active layer thickness (ALT) in an area of the Italian Central Alps where a few information about the ALT is present. Looking at a particular warm year (2018), we improved PERMACLIM, a model used to calculate the Ground Surface Temperature (GST) and applied two different versions of Stefan's equation to model the ALT. PERMACLIM was updated refining the temporal basis (daily respect the monthly means) of the air temperature and the snow cover. PERMACLIM was updated also to minimize the bias of the snow cover in summer months using the PlanetScope images. Moreover, the contribution of the solar radiation was added to the air temperature to improve the summer GST. The modelled GST showed a good calibration and, among the two versions of Stefan's equation, the first (ALT1) indicates a maximum active layer thickness of 7.5 m and showed a better accuracy with R2 of 0.93 and RMSE of 0.32 m. The model underlined also the importance of better definition of the thermal conductivity of the ground that can strongly influence the ALT.

Human disturbance in the Arctic is increasing. Abrupt changes in vegetation may be expected, especially when spots without vegetation are made available; additionally, climate change alters competition between species. We studied whether 34- to 35-year-old seismic operations had left imprints on local vegetation and whether changes could be related to different soil characteristics. The study took place in Jameson Land in central east Greenland where winter seismic operations in search of oil took place from 1985 to 1989. This area is dominated by continuous dwarf shrub heath with Cassiope tetragona, Betula nana, and Vaccinium uliginosum as dominant species. Using point frame analyses, we registered vascular plants and other surface types in frames along 10-m transects in vehicle tracks (hereafter damages) and in undisturbed vegetation parallel to the track (hereafter references) at eleven study sites. We also measured temperature and pH and took soil samples for analysis. Damaged and reference vegetation types were compared with S & oslash;rensen similarity indices and detrended correspondence analyses. Although most vascular plant species were equally present in damaged vegetation and in references the detrended correspondence analyses showed that at ten out of eleven study sites the damages and references still differed from each other. Graminoids and the herb Polygonum viviparum had the highest occurrence in damages. Shrubs and the graminoid Kobresia myosuroides had the highest occurrence in references. Cassiope tetragona was negatively impacted where vehicles had compacted the snow. Moss, organic crust or biocrust, soil, and sand occurred more often in damages than in references, whereas lichens and litter had the highest occurrence in references. The richness of vascular plant species varied between the eleven study sites, but between damages and references the difference was only up to four species. Temperature was the soil parameter with the most significant differences between damages and references. Total recovery of the damaged vegetation will most likely not occur within several decades. The environmental regulations were important to avoid more serious impacts.

Liquefaction hazard analysis is crucial in earthquake-prone regions as it magnifies structural damage. In this study, standard penetration test (SPT) and shear wave velocity (Vs) data of Chittagong City have been used to assess the liquefaction resistance of soils using artificial neural network (ANN). For a scenario of 7.5 magnitude (Mw) earthquake in Chittagong City, estimating the liquefaction-resistance involves utilizing peak horizontal ground acceleration (PGA) values of 0.15 and 0.28 g. Then, liquefaction potential index (LPI) is determined to assess the severity of liquefaction. In most boreholes, the LPI values are generally higher, with slightly elevated values in SPT data compared to Vs data. The current study suggests that the Valley Alluvium, Beach and Dune Sand may experience extreme liquefaction with LPI values ranges from 9.55 to 55.03 and 0 to 37.17 for SPT and Vs respectively, under a PGA of 0.15 g. Furthermore, LPI values ranges from 25.55 to 71.45 and 9.55 to 54.39 for SPT and Vs correspondingly. The liquefaction hazard map can be utilized to protect public safety, infrastructure, and to create a more resilient Chittagong City.

Correlations between the mechanical properties and surface scratch resistance of polylactic acid (PLA) are investigated via tensile and scratch tests on samples after degradation in soil for various times. The results show that the tensile yield strength of PLA is inversely proportional to the natural logarithm of the degradation time, and the scratch resistance and fracture toughness of PLA and the temperature rise near the indenter all increase and then decrease. The surface crystallinity of PLA also increases and then decreases, indicating that it and the scratch resistance are closely related. These findings provide useful information about how PLA behaves under degradation conditions. (c) 2025 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC) license (https://creativecommons.org/licenses/by-nc/4.0/).

To investigate the effect of interface temperature on the soil-reinforcement interaction mechanism, a series of pullout tests were conducted considering different types of reinforcement (geogrid and non-woven geotextile), backfill (dry sand, wet sand, and clay), and six interface temperatures. The test results indicate that at interface temperatures of 0 degrees C and above, reinforcement failure didn't occur during the pullout tests, whereas it predominantly occurred at subzero temperatures. Besides, the pullout resistance for the same soil-reinforcement interface gradually decreased as the interface temperature rose. At a given positive interface temperature, the pullout resistance between wet sand and reinforcement was significantly higher than that of the clayreinforcement interface but lower than that of the dry sand-reinforcement interface. Compared with geotextile reinforcements, geogrids were more difficult to pull out under the same interface temperature and backfill conditions. In addition, the lag effect in the transfer of tensile forces within the reinforcements was significantly influenced by the type of soil-reinforcement interface and the interface temperature. Finally, the progressive deformation mechanism along the reinforcement length at different interface temperatures was analyzed based on the strain distribution in the reinforcement.

Study region: The Tibetan Plateau (TP), China, contains the world's largest permafrost area outside the Polar Regions. Study focus: This study investigates the precipitation-induced advective heat flux (E-Pre), which represents the energy transfer resulting from the temperature difference between rainfall and soil. Observational data from three permafrost monitoring sites (Qumalai, Xidatan, and Tanggula) were combined with simulations from the Community Land Model version 5.0 (CLM5.0) to quantify E-Pre precipitation infiltration depth, and the probability of infiltration reaching the frozen soil layer. The analysis further examines how precipitation amount, soil texture, soil moisture, and freeze-thaw state jointly control infiltration processes and influence the soil thermal regime. New hydrological insights for the region: Infiltration depth varies with initial soil moisture and precipitation duration, from shallow retention to deep percolation. E-Pre is generally negative, with maximum cooling of-84.14 W m(-2) at QML,-73.24 W m(-2) at XDT, and -56.63 W m(-2) at TGL, but becomes positive during prolonged summer rainfall, reaching 45.43 W m(-2) at QML. Diurnal soil temperature variations shift E-Pre from cooling by day to reduced cooling or warming at night. Across the TP, mean infiltration depth is similar to 5 cm, higher in southeastern Tibet, with a regional mean E-Pre of-0.08 W m(-2). Warming effects are concentrated in the southeastern and central TP, while cooling dominates the arid west and high-elevation north.

The presence of frozen volatiles (especially H2O ice) has been proposed in the permanently shadowed regions (PSRs) near the poles of the Moon, based on various remote measurements including the visible and near-infrared (VNIR) spectroscopy. Compared with the middle- and low-latitude areas, the VNIR spectral signals in the PSRs are noisy due to poor solar illumination. Coupled with the lunar regolith coverage and mixing effects, the available VNIR spectral characteristics for the identification of H2O ice in the PSRs are limited. Deep learning models, as emerging techniques in lunar exploration, are able to learn spectral features and patterns, and discover complex spectral patterns and nonlinear relationships from large datasets, enabling them applicable on lunar hyperspectral remote sensing data and H2O-ice identification task. Here we present H2O ice identification results by a deep learning-based model named one-dimensional convolutional autoencoder. During the model application, there are intrinsic differences between the remote sensing spectra obtained by the orbital spectrometers and the laboratory spectra acquired by state-of-the-art instruments. To address the challenges of limited training data and the difficulty of matching laboratory and remote sensing spectra, we introduce self-supervised learning method to achieve pixel-level identification and mapping of H2O ice in the lunar south polar region. Our model is applied to the level 2 reflectance data of Moon Mineralogy Mapper. The spectra of the identified H2O ice-bearing pixels were extracted to perform dual validation using spectral angle mapping and peak clustering methods, further confirming the identification of most pixels containing H2O ice. The spectral characteristics of H2O ice in the lunar south polar region related to the crystal structure, grain size, and mixing effect of H2O ice are also discussed. H2O ice in the lunar south polar region tends to exist in the form of smaller particles (similar to 70 mu m in size), while the weak/absent 2-mu m absorption indicate the existence of unusually large particles. Crystalline ice is the main phase responsible for the identified spectra of ice-bearing surface however the possibility of amorphous H2O ice beneath optically sensed depth cannot be ruled out.