Through analyzing the triggering factors and activity characteristics of avalanches in Aerxiangou in the Western Tianshan Mountains, the formation and disaster-causing process of avalanches were studied to provide theoretical support and a scientific basis for avalanche disaster prevention. In this paper, based on remote sensing interpretation and field investigation, a spatial distribution map of avalanches was established, and the induced and triggering factors in disaster-prone environments were analyzed using the certainty factor model. The degree of influence (E) of the disaster-causing factors on avalanche triggering was quantified, and the main control conditions conducive to avalanche occurrence in different periods were obtained. The RAMMS-avalanche model was used to analyze the activity characteristics at points where multiple avalanches occurred. Research results: (1) The E values of the average temperature, average snowfall, and surface roughness in February were significantly higher than those of other hazard-causing factors, reaching 1.83 and 1.71, respectively, indicating strong control. The E values of the surface cutting degree, average temperature, and average snow depth in March were all higher than 1.8, indicating that these control factors were more prominent than the other factors. In contrast, there were four hazard-causing factors with E values higher than 1.5 in April: the mean temperature, slope, surface roughness, and mean wind speed, with clear control. (2) Under the influence of the different hazard-causing factors, the types of avalanches from February–April mainly included new full-layer avalanches, surface avalanches, and full-layer wet avalanches. (3) In the RAMMS-avalanche simulation test, considering the deposition effect, compared to the previous avalanche movement path, the secondary avalanche flow accumulation area impact range changes were slight, while the movement area within the avalanche path changes was large, as were the different categories of avalanches and their different movement characteristic values. Overall, wet snow avalanches are more hazardous, and the impact force is larger. The new snow avalanches start in a short period, the sliding rate is fast, and the avalanche sliding surface (full-snow surface and face-snow) of the difference is mainly manifested in the differences in the value of the flow height.

Avalanches occur frequently in mountainous areas and pose significant threats to roads and infrastructure. Clarifying how terrain conditions influence avalanche initiation and movement is critical to improving hazard assessment and response strategies. This study focused on a wet-snow slab avalanche that occurred on 26 March 2024, in the Simutas region of the northern Tianshan Mountains, Xinjiang, China. The authors combined remote sensing imagery, high-resolution meteorological station observations, field investigations, and numerical simulations (RAMMS::Avalanche) to analyze the avalanche initiation mechanism, dynamic behavior, and path recurrence characteristics. Results indicated that persistent heavy snowfall, rapid warming, and substantial daily temperature fluctuations triggered this avalanche. The predominant southeasterly (SE) winds and the northwest-facing (NW) shaded slopes created favorable leeward snow deposition conditions, increasing snowpack instability. High-resolution meteorological observations provided detailed wind, temperature, and precipitation data near the avalanche release zone, clearly capturing snowpack evolution and meteorological conditions before avalanche initiation. Numerical simulations showed a maximum avalanche flow velocity of 19.22 m/s, maximum flow depth of 12.42 m, and peak dynamic pressure of 129.3 kPa. The simulated avalanche deposition area and depth closely matched field observations. Multi-temporal remote sensing images indicated that avalanche paths in this area remained spatially consistent over time, with recurrence intervals of approximately 2–3 years. The findings highlight the combined role of local meteorological processes and terrain factors in controlling avalanche initiation and dynamics. This research confirmed the effectiveness of integrating remote sensing data, high-resolution meteorological observations, and dynamic modeling, providing scientific evidence for avalanche risk assessment and disaster mitigation in mountain regions.

Avalanche susceptibility assessment is a core aspect of regional avalanche early warning and risk analysis and is of great significance for disaster prevention and mitigation on proposed highways. Using sky–ground integration investigation, 83 avalanche points within the G219 Wen Quan to Horgos transportation corridor were identified, and the avalanche hazard susceptibility of the transportation corridor was partitioned using the certainty factor (CF) model and the coupled coefficient of the certainty factor–Geodetector (CF-GD) model. The CF model analysis presented nine elements of natural conditions which influence avalanche development; then, by applying the Geodetector for each of the factors, a weighting coefficient was given depending on its importance for avalanche occurrence. The results demonstrate the following: (1) According to the receiver operating characteristic (ROC) curve used to verify the accuracy, the area under the ROC curve (AUC) value for the CF-GD coupled model is 0.889, which is better than the value of 0.836 of the CF model’s evaluation accuracy, and the coupled model improves the accuracy by about 6.34% compared with the single model, indicating that the coupled model is more accurate. The results provide avalanche prevention and control recommendations for the G219 Wen Quan to Horgos transportation corridor. (2) The slope orientation, slope gradient, and mean winter temperature gradient are the main factors for avalanche development in the study area. (3) The results were validated based on the AUC values. The AUCs of the CF-GD coupled model and the CF model were 0.889 and 0.836, respectively. The accuracy of the coupled model was improved by about 6.34% compared to the single model, and the coupled CF-GD model was more accurate. The results provide avalanche control recommendations for the G219 Wen Quan to Horgos transportation corridor.

Based on a specially designed visualization pullout system and digital photographic measurement technology, geogrid pullout tests were conducted by varying the top load, geogrid type, coarse grain content, and particle shape. The evolution and distribution of the reinforcement influence zone and the soil particle displacement field were analyzed, and the effects of various factors on the formation speed of the reinforcement influence zone, gradient layer thickness, and fine-scale particle displacement characteristics were discussed. The study shows that the reinforcement influence zone’s basic form and particle displacement direction do not change with pullout displacement after it is fully developed. The displacement layers in the influence zone are centered at the reinforced soil interface and are distributed in a diffusion gradient. The thickness of each gradient layer in the upper influence zone is greater than that in the lower influence zone. The greater the normal load is, the smaller the particle displacement and thickness of each gradient layer, and the slower the formation of the reinforcement influence zone. Using high-strength geogrids and geogrids with nodes can increase the upper interface thickness and improve the reinforcement influence zone’s formation speed. Horizontal ribs play a major role in forming the reinforcement influence zone, while longitudinal ribs mainly affect the formation speed. The indirect reinforcement effect of the geogrid on angular gravel soil is better than that on pebble soil. As the coarse grain content in the fill increases from 20% to 30%, the reinforcement influence zone forms faster, and the particle displacement of each gradient layer is smaller. When the coarse grain content increases from 30% to 35%, there is no significant change in the forming rate of the reinforcement influence zone.

Centrifuge model tests were conducted to investigate the geogrid-gravelly-soil-reinforced steep slope embankments. A control experiment was carried out on a set of unreinforced embankments. The following test results were obtained. Under a centrifugal force, the unreinforced gravelly soil steep embankments failed in sudden collapse and circular sliding. By contrast, the reinforced embankments exhibited a local deformation on the slope surface and a continuous progressive deformation on the slope due to the failure of geogrid-soil interface. The geogrid reinforcement strengthened the soil and enhanced the integrity of the embankment. Thus, the stability of the slope was improved, and the horizontal lateral displacement of the slope and the settlement at the slope crest were reduced. In the design of a geogrid, reinforcements should be sparsely arranged in the upper section of the embankment and densely arranged in the lower section. For equally spaced reinforcements, the geogrid should be strengthened in the middle and lower sections of the embankment.

The Qinghai-Tibetan Plateau (QTP) has undergone an increase in rainfall and a drastic alteration in the moisture-heat regime in active layers and engineering. To investigate the water and heat responses of natural ground and engineering to rainfall, the differences in energy on the ground surface and the thermal-moisture dynamics of different permafrost underlying surfaces were discussed. Based on the meteorological data in 2013 observed at the Beiluhe observation station, three types of underlying surfaces (i.e., natural ground, asphalt pavement, and gravel pavement) were selected to compare the differences in energy balance at the ground surface, water-energy transport process, and coupling mechanism in active layers under rainfall conditions by a coupled water vapor-heat model of the unsaturated frozen soil. The results show that the asphalt pavement greatly increases the surface net radiation and soil surface heat flux, decreases the surface evaporation latent heat, and cuts off the moisture migration between the atmosphere and the active layer. The gravel pavement significantly increases the surface evaporation latent heat to lower the soil surface heat flux, and the amount of moisture in shallow soil is strongly influenced by rainfall and evaporation. Therefore, the moisture migration and accumulation under the asphalt pavement are dominated by the water vapor flux under thermal gradients, whereas the liquid water under the water potential gradients is the major source of moisture migration under the gravel pavement. The heat transfer in the shallow active layer is dominated by heat conduction. The effect of heat conduction, water vapor migration, and phase transition on the soil temperature is evident for the asphalt pavement, while the impact of liquid water migration on the shallow soil temperature for the gravel pavement is significant in the thawing period. As a result, the soil temperature relationship between different underlying surfaces is asphalt pavement>gravel pavement>natural ground. The thickness of the active layer gradually decreases. Although rainfall infiltration promotes the liquid water convection of the gravel pavement, the decrease in heat flux is less than the increase in thermal conductivity. In general, the construction of asphalt pavement and gravel pavement accelerates the degradation of permafrost. The results can provide theoretical and simulated guidance for the stability prediction and analysis of various underlying surfaces in the central QTP where rainfall is increasing.