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.

Freight transportation plays a crucial role in sustaining the Canadian economy. However, heavy truck transportation also puts enormous pressure on roadway networks. Spring Load Restrictions (SLR) are implemented to minimize road damage caused by heavy traffic during the thaw-weakening season, and Winter Weight Premium (WWP) is used to reduce the impact of SLR on trucking operations by allowing higher axle loads in winter. However, existing policies apply fixed dates each year for these restrictions, regardless of the actual structural capacity of the pavement. Different methods have been proposed to improve the application of SLR and WWP; however, they rely mainly on indirect indices, such as the cumulative thawing index and cumulative freezing index, which pose challenges in their calculation. This study explores the practical implementation of machine learning models for accurately determining the start and end dates of SLR and WWP. In a novel approach, machine learning models directly derive the start and end dates of SLR and WWP from frost and thaw depths in the pavement structure which are determined by pavement temperature and moisture content. In contrast to previous studies that neglected the influence of soil moisture content on determining the start and end dates of SLR and WWP, this study examines the variation in soil moisture content to evaluate the validity of existing theories. The findings reveal a high level of agreement between the machine learning model's estimations of frost and thaw depths and the measured values, with R2 values exceeding 0.91.

Global warming is likely to transform Siberian environments. Recent eco-hydrological evidence indicates that water and carbon cycles have been changing rapidly, with potentially serious effects on the Siberian flora and fauna. We have comprehensively analysed dendrochronological, hydrological, and meteorological data and satellite remote sensing data to track changes in vegetation and the water and carbon cycles in the Lena River Basin, eastern Siberia. The basin is largely covered with larch forest and receives little precipitation. However, from 2005 to 2008 the central part of the basin experienced an extraordinarily high level of precipitation in late summer and winter. This resulted in the degradation of permafrost, forest, and hydrological elements in the region. Dendrochronological data implied that this event was the only incidence of such conditions in the previous 150 years. Based on data collected before and after the event, we developed a permafrost-ecosystem model, including surface soil freeze-thawing processes, to better represent the heat, water, and carbon fluxes in the region. We focused on the surface soil layer, in which an increased thawing depth is now apparent, surface soil moisture, and net primary production. An analysis of observed and model-simulated data indicated that the annual maximum thawing depth (AMTD) had increased gradually on a decadal scale and deepened abruptly after 2005. Climatological analyses of atmospheric water circulation over the region indicated that the recent increases in precipitation over the central Lena River Basin were partly related to cyclone activity. Consequently, the increased precipitation from late-summer to winter resulted in increases in soil moisture, soil temperature, and AMTD in the region.