Freeze-thaw cycles (FTC) influence soil erodibility (K-r) by altering soil properties. In seasonally frozen regions, the coupling mechanisms between FTC and water erosion obscure the roles of FTC in determining soil erosion resistance. This study combined FTC simulation with water erosion tests to investigate the erosion response mechanisms and key drivers for loess with varying textures. The FTC significantly changed the mechanical and physicochemical characteristics of five loess types (P < 0.05), especially reducing shear strength, cohesion, and internal friction angle, with sandy loam exhibiting more severe deterioration than silt loam. Physicochemical indices showed weaker sensitivity to FTC versus mechanical properties, with coefficients of variation below 5 %. Wuzhong sandy loess retained the highest K-r post-FTC, exceeding that of the others by 1.04 similar to 2.25 times, highlighting the dominant role of texture (21.37 % contribution). Under different initial soil moisture contents (SMC), K-r increased initially and then stabilized with successive FTC, with a threshold effect of FTC on K-r at approximately 10 FTC. Under FTC, the K-r variation rate showed a concave trend with SMC, turning point at 12 % SMC, indicating that SMC regulates freeze-thaw damage. Critical shear stress exhibited an inverse response to FTC compared to K-r, displaying lower sensitivity. The established K-r prediction model achieved high accuracy (R-2 = 0.87, NSE = 0.86), though further validation is required beyond the design conditions. Future research should integrate laboratory and field experiments to expand model applicability. This study lays a theoretical foundation for research on soil erosion dynamics in freeze-thaw-affected areas.

This study presents a new experimental procedure for evaluating the durability of stabilized soils subjected to multiple wetting and drying (W-D) cycles. An integrated experimental program combining dynamic shear rheometer (DSR) testing with W-D cycles was designed and implemented to assess moisture-induced performance degradation in natural sand stabilized with two types of rapid-setting cementitious stabilizers. Small cylindrical specimens (10.5 mm in diameter and 35.0 mm in height) of stabilized sand mixes were compacted, cured, and subjected to up to seven W-D cycles. Each W-D cycle was meticulously controlled to gauge its impact on the material's durability. The mechanical properties of the stabilized samples were evaluated at different stages of the W-D cycles using the strain-sweep DSR testing based on a methodology developed from preliminary work. The proposed test method focuses on the shear properties of the material, measuring its mechanical response under the torsional loading of a cylindrical sample and providing dynamic mechanical properties and fatigue-resistance characteristics of the stabilized soils under cyclic loading. Test results demonstrate water-induced deterioration of stiffness and reduced resistance to cyclic loading with good testing repeatability, efficiency, and material-specific sensitivity. By combining dynamic mechanical characterization with durability assessment, the new testing method provides a high potential as a simple, scientific, and efficient method for assessing, engineering, and developing stabilized soils, which will enable more resilient transportation infrastructure systems.

The seismic performance of underground structures is strongly influenced by the characteristics of both the surrounding soil and the earthquake. In contrast to traditional deterministic analysis methods, this study uses a stochastic analysis approach to investigate the effect of uncertainties in nonlinear soil characteristics, shear wave velocity, density, and earthquake randomness on the response of underground stations. The equivalent linearization method is employed to approximate the nonlinear behavior of the soil. The soil was modeled using a linear elastic constitutive model combined with Rayleigh damping in the finite element model. Inter-story displacements are used to determine structural damage. Probabilistic analysis methods are used to obtain their statistical characteristics, and the probability of failure is calculated. The results show that, according to single parameter analysis, random ground motion results in the greatest probability of exceeding the threshold (PET), while ground shear wave velocity significantly affects the coefficient of variation (COV), and the effect of density is the smallest. The study also found that when soil nonlinearity, shear wave velocity, and random ground motion are considered simultaneously, the range, mean, standard deviation, and COV of interstory displacement all increase significantly, but the PET slightly decreases. In summary, the analysis results indicate that random ground motion has the greatest impact on interstory displacement, followed by shear wave velocity, with nonlinear soil characteristics having a smaller effect, and density the least. Therefore, the impact of various uncertainties should be fully considered in the analysis of underground structures, especially random ground motion and shear wave velocity.

This study demonstrates the feasibility of utilizing machine learning (ML) for routine identification of sand particles. Identifying different types of sand is necessary for various geotechnical exploration projects because understanding the specific sand type plays an important role in estimating the physical and mechanical properties of the soil. To accomplish this, dynamic image analysis was employed to generate a substantial volume of sand particle images. Individual size and shape descriptors were automatically extracted from each particle image. The analysis involved use of 40,000 binary particle images representing 20 different sand types, and a corresponding six size and four shape descriptors for each particle (400,000 parameters). Six ML models were trained and tested. The work demonstrates that using size and shape features the models efficiently identified up to 49% of individual sand particles. However, when clusters of particles were considered in conjunction with a voting algorithm, classification accuracy significantly improved to 90%. Among the ML models studied, neural networks performed the best, while decision tree exhibited the lowest accuracy. Finally, the use of size consistently outperformed shape as a classification parameter but combining size and shape parameters yielded superior results across all sands and classifiers. These findings suggest that ML holds much promise for automating sand classification using ordinary images.

Coastal wetlands are extremely vulnerable to both marine damage and human activities. In order to protect these wetlands, many artificial seawalls have been constructed. However, studies are required to understand how coastal wetlands will evolve under the influence of artificial seawalls. Therefore, to understand this succession process of plants and their adaptation to habitats divided by seawalls, two different habitats inside and outside the seawalls were selected in Laizhou Bay, China. The results showed that there were 5 plant species outside the seawalls that were lower than the 13 species inside. Additionally, the dominant plant species were varied between the two habitats, with mostly annual herbs observed outside the seawalls and perennial shrubs inside. Soil salinity was higher outside the seawalls, which was the key impact factor of soil nutrient differences. The distribution of annual and perennial species may be constrained by spatial differences in soil stoichiometry. Therefore, the plants in coastal wetlands vary significantly at a small scale in response to the disturbance of artificial seawalls. The differences in soil and plants between the two habitats divided by the artificial seawalls provide a new insight for evaluating the artificial coastal projects. The only way to reduce the effects of seawalls on natural coastal wetland vegetation and ecosystem functions is to restore connectivity of tidal flow inside and outside the seawalls.

Streambed scour in cohesive sediment is complex because erosion processes depend on the physical, geochemical, and biological properties of the sediment. The scouring processes can also be characterized as a slow fatigue phenomenon. Therefore, repetitive hydraulic loadings from multiple stormflow events are likely necessary for equilibrium scour depths to develop in cohesive sediment compared with non-cohesive sediment. Cumulative effective stream power, which is a surrogate measure of effective stream power duration, showed a significant relation with scour development and propagation in cohesive sediments around bridge piers, where results from this study identified a statistically significant correlation between cumulative effective stream power and the observed scour depths around different bridge piers (R-2 = 0.56, p < 0.001). However, some localized and site-specific variations were observed. It was also observed that scour depth development in cohesive soil appeared to be dependent on effective shear duration, rather than the number of flow events above erosion threshold values. In addition, the relationship between an erodibility index (K) and critical stream power showed a significant statistical correlation (R-2 = 0.61, p = 0.017). Results from this study deviated from the Annandale empirical relationship for sediments when K < 0.1. This finding supports that site-specific critical stream power should be measured using an empirical relationship for cohesive bed sediments to predict scour depths.

Currently, little is known about the spatial variability of significant soil properties and their relationships to forest ecosystems of different vegetation grades. This work evaluates the variability of the properties of the upper layer of Cambisol taxa and their relationship to altitude and forest ecosystems of 2nd to 5th forest vegetation grades selected in the Western Carpathians using PCA and regression analysis. The content of clay, total carbon and total nitrogen, humus, energy, and ash in the soils varied between 5.43 and 11.53 %, 21-65 mg g-1, 1.9-4.7 mg g-1, 36-112 mg g-1, 438.4-5845.7 J g- 1 and 852.9-946.3 mg g-1, and C/N, pHH2O, and pHKCl values ranged between 11.2 and 16.7, 4.0-5.8 and 3.1-4.6. PCA showed that EAC in the 3rd oak-beech vegetation grade had significantly higher pH values and significantly lower energy content, ESC in the 4th beech vegetation grade had a significantly higher ash content and a significantly lower energy content, and DC in the 5th fir-beech vegetation grade had a significantly higher content of Ct, Nt, and humus. Linear regression revealed a strong negative correlation between the energy content and soil reaction (R2 for pHH2O = 0.48; R2 for pHKCl = 0.38) for all Cambisol taxa. Ct content and ash show a strong negative correlation (R2 = 0.78). The positive relationship between altitude and FVGs was found only for the soil Ct (R2 = 0.87), Nt (R2 = 0.81), and humus content (R2 = 0.87). A strong negative linear relationship between altitude and FVGs showed the ash content (R2 = 0.77). In turn, the oscillatory, polynomial course had a relationship between the clay content (R2 = 0.65) and energy (R2 = 0.75) to altitude and FVGs. Recognizing significant soil variables and better understanding their impact on the development of forest ecosystems is a prerequisite for distinguishing areas with the highest risk of their damage under conditions of various anthropogenic interventions and climate change. Therefore, this topic continues to require increased research efforts. For this reason, a better understanding of the relationships between soil properties and ecologically differentiated communities of forest ecosystems will allow us to identify areas with the highest risk of ecological changes that could lead to the degradation of European forests in the future.

Climate change is destabilizing permafrost landscapes, affecting infrastructure, ecosystems, and human livelihoods. The rate of permafrost thaw is controlled by surface and subsurface properties and processes, all of which are potentially linked with each other. However, no standardized protocol exists for measuring permafrost thaw and related processes and properties in a linked manner. The permafrost thaw action group of the Terrestrial Multidisciplinary distributed Observatories for the Study of the Arctic Connections (T-MOSAiC) project has developed a protocol, for use by non-specialist scientists and technicians, citizen scientists, and indigenous groups, to collect standardized metadata and data on permafrost thaw. The protocol introduced here addresses the need to jointly measure permafrost thaw and the associated surface and subsurface environmental conditions. The parameters measured along transects include: snow depth, thaw depth, vegetation height, soil texture, and water level. The metadata collection includes data on timing of data collection, geographical coordinates, land surface characteristics (vegetation, ground surface, water conditions), as well as photographs. Our hope is that this openly available dataset will also be highly valuable for validation and parameterization of numerical and conceptual models, and thus to the broad community represented by the T-MOSAiC project.