On December 18, 2023, a magnitude MS6.2 earthquake struck Jishishan County, Gansu Province, triggering over 40 seismic subsidence sites within a seismic intensity VI zone, 32 km from the epicenter.The earthquake caused tens of millions in economic losses to mountain photovoltaic power stations. Extensive geological surveys and comparisons with similar landslides (such as soil loosening, widespread cracks, and stepped displacements) triggered by the 1920 Haiyuan MS8.5 earthquake and the 1995 Yongdeng MS5.8 earthquake, this study preliminarily identifies one subsidence sites as a seismic-collapsed loess landslide. To investigate its disaster-causing mechanism: the dynamic triaxial test was conducted to assess the seismic subsidence potential of the loess at the site, and the maximum subsidence amount under different seismic loads were calculated by combining actual data from nearby bedrock stations with site amplification data from the active source; simulation of the destabilization evolution of seismic-collapsed loess landslides by large-scale shaking table tests; and a three-dimensional slope model was developed using finite element method to study the complex seismic conditions responsible for site damage. The research findings provide a theoretical foundation for further investigations into the disaster mechanisms of seismic-collapsed loess landslides.

Predicting cumulative surface slope displacements induced by rainfall infiltration is crucial for accurately assessing the risks to potentially affected infrastructure. In this paper the numerical modelling of the case history of Miscano slope is presented. Plaxis 2D code has been used adopting two constitutive laws: the linear elastoplastic model (Mohr-Coulomb, MC) and the Hardening Soil with small strain stiffness (HSsmall). The aim is to test the suitability of these constitutive laws in predicting the hydro-mechanical behaviour of clayey soil slope. Based on long-term field measurements, the parameters of MC and HSsmall have been determined by back analysing the first-year field measurements in terms of cumulative surficial horizontal displacements and pore water pressure. Subsequently, the numerical models have been validated against the analogous field measurements collected from the second year. The numerical models predict with a good agreement the field measurements for both years. In terms of cumulative surficial horizontal displacements, the HSsmall underestimates the field measurements by 21.2% at the end of the first year, while that based on MC exhibits a 32.8% overestimation. Moreover, the initialization procedure clearly affects the cumulative surficial horizontal displacements results obtained with both the HSsmall and MC models for the second year. In fact, the best results have been achieved when the second-year net rainfall have been applied starting from the initial phase used to generate the lithostatic stress state.

Climate change increases the frequency of extreme weather events, intensifying shallow flow-type landslides, soil erosion in mountainous regions, and slope failures in coastal areas. Vegetation and biopolymers are explored for ecological slope protection; however, these approaches often face limitations such as extended growth cycles and inconsistent reinforcement. This study investigates the potential of filamentous fungi and wheat bran for stabilizing loose sand. Triaxial shear tests, disintegration tests, and leachate analyses are conducted to evaluate the mechanical performance, durability, and environmental safety of fungus-treated sand. Results show that the mycelium enhances soil strength, reduces deformation, and lowers excess pore water pressure, with a more pronounced effect under undrained than drained conditions. Mycelium adheres to particle surfaces, forming a durable bond that increases cohesion and shifts the slope of the critical state line, significantly enhancing the mechanical stability of fungus-treated sand. The resulting strength parameters are comparable to those of soils reinforced with plant roots. Fungus-treated sand remains stable after 14 days of water immersion following triaxial shear tests, with no environmental risk from leachate. These findings demonstrated that fungal mycelium provides an effective and eco-friendly solution for stabilizing loose sand, mitigating shallow landslides, and reinforcing coastlines.

From July 26 to July 28, 2024, a rare heavy rainfall associated with Typhoon Gaemi triggered widespread clustered landslides in Zixing City, Hunan Province, China. The severe disaster caused 50 fatalities and 15 missing persons across 26 villages, damaging 11,869 houses and affecting a total of 128,000 individuals. Timely and accurate event analysis is essential for deepening our understanding of landslide clustering mechanisms and guiding future disaster prevention efforts. To achieve this, remote sensing analysis using satellite and unmanned aerial vehicle (UAV) aerial images was conducted to assess the distribution pattern of landslide clusters and explore their relationship with environmental factors. Field investigations were subsequently carried out to identify the failure mechanisms of representative landslides. The results identified three main landslide clustering areas in the eastern mountainous forest region of Zixing City. The landslides are predominantly shallow soil slides, with their distribution closely linked to rainfall thresholds and lithology. The clustering areas typically received cumulative precipitation exceeding 400 mm during the extreme rainfall event. Lithology significantly influences the composition and thickness of slope soils, which in turn controls sliding patterns and affects landslide distribution density and individual landslide size. Granite residual soils contributed to the highest landslide density, with many large individual landslides. Topography and vegetation also play important roles in landslide formation and movement. This study provides preliminary insights into the clustered landslide event, aiding researchers in quickly understanding its key features.

Current practice to model the occurrence of submarine landslides is based on methods that assess the potential of site-specific failures, all with the objective of providing elements to identify and quantify regional features associated to geohazards, before a project development takes place. Also, survey data to estimate parameters required to model submarine landslides show typically limited availability, mainly because of the cost associated to offshore surveying campaigns. In this paper, a probabilistic calibration approach is introduced using Bayesian statistical inference to maximize the use of available site investigation data, and to best estimate the occurrence of a marine landslide. For this purpose, a landslide model thought for its simplicity is used to illustrate the applicability and potential of the calibration methodology. The aim is to introduce a systematic approach to produce prior probability distributions of the model parameters, based on an actual integrated marine site investigation including geological, geophysical, and geomatics data, to then compare it with a posterior probability distribution of the same model parameters, but estimated after collecting in situ soil samples and testing them in the laboratory to produce the corresponding soil strength properties. This comparison allows to explore (a) the influence of the number of in situ samples, (b) the influence of a landslide factor of safety, and (c) the influence of the soil heterogeneity, into the likelihood of the occurrence of a marine landslide. The model parameters that are considered for calibration include the initial state of the submerged and saturated soil unit weight, the thickness of the soils' unit layers, the pseudo-static seismic coefficient, and the slope angle, while the soil undrained shear strength is considered as the reference parameter to conduct the calibration (i.e., to compare model predictions vs. actual observations). Results show the potential of the proposed methodology to produce landslide geohazard maps, which are needed for the planning and design of marine infrastructure.

With the continued development of water resources in Southwest China, fluctuations in water levels and rainfall have triggered numerous landslides. The potential hazards posed by these events have garnered considerable attention from the academic community, making it imperative to elucidate the landslide mechanisms under the combined influence of multiple factors. This study integrates laboratory tests and numerical simulations to explore the instability mechanisms of landslides under the combined effects of rainfall and fluctuating water levels, as well as to compare the impacts of different factors. Results indicate that the sensitivity of landslide deformation decreases as the number of water level fluctuations increases, exhibiting a gradually stabilizing tendency. However, the occurrence of a heavy rainfall event can reactivate previously stabilized landslides by increasing pore water pressure and establishing a positive feedback loop with rainfall infiltration. This process reduces boundary constraints at the toe of the slope, promotes the development of an overhanging surface, and ultimately leads to overall instability and landslide disaster. Under the same rainfall intensities, the presence of water level fluctuations prior to rainfall significantly shortens the time for the landslide to reach a critical state. The key mechanisms contributing to landslide failure include terrain modification, fine particle erosion, and outward water pressure, all of which generates substantial destabilizing forces. This research offers valuable insights for the monitoring, early warning, and risk mitigation of landslides that have already experienced some degree of deformation in hydropower reservoir areas.

In this paper a three-dimensional agro-hydrological model for shallow landslides' prediction is presented. The model is an extension of the CRITERIA-3D free-source model for crop development and soil hydrology, developed by the Hydrometeorological service of the Regional Agency for Environmental prevention and Energy of EmiliaRomagna region (Arpae-simc). The soil-water balance is computed through the coupling of surface and subsurface flows in multi-layered soils over areas topographically characterized by Digital Elevation Model (DEM). The rainfall infiltration process is simulated through a three-dimensional version of Richards' equation. Surface runoff, lateral drainage, capillarity rise, soil evaporation and plant transpiration contribute to the computation of the soil hydrology on an hourly basis. The model accepts meteorological hourly records as input data and outputs can be obtained for any time step at any selected depth of the soil profile. Among the outputs, volumetric water content, soil-water potential and the factor of safety of the slope can be selected. The validation of the proposed model has been carried out considering a test slope in Montue` (northern Italy), where a shallow landslide occurred in 2014 a few meters away from a meteorological and soil moisture measurement station. The paper shows the accuracy of the model in predicting the landslide occurrence in response to rainfall both in time and space. Although there are some model limitations, at the slope scale the model results are highly accurate with respect to field data even when the spatial resolution of the Digital Elevation Model is reduced.

Landslides are one of the most hazardous geological processes due to their difficult-to-predict nature and destructive effects, often leading to significant loss of life, infrastructure damage, and environmental disruption. In the Southern Andes of Chile, landslides are particularly frequent and destructive due to a combination of factors, including high seismic activity, steep topography, and the presence of weak, unconsolidated pyroclastic soils. Unfortunately, the geomechanical control of landslide initiation in the Southern Andes is still poorly understood, creating a significant source of uncertainty in developing accurate landslide susceptibility or risk models. This study evaluates the geological and geotechnical factors that control the generation of landslides in pyroclastic soils using in situ data, laboratory analysis and remote sensing approaches. The study area covers the surroundings of the Mocho-Choshuenco Volcanic Complex (MCVC), one of the most explosive volcanoes in the Southern Andes. The results show that the landslides are placed on slopes covered by multiple explosive eruptions that include a period of more than 12 ka. Landslide activity is related to pyroclastic soils with significant weathering and halloysite content. In addition, the geotechnical characteristics show very light soils, with highwater retention capacity, which is vital to induce mechanical instability. The detected deformation may be associated with seasonal precipitation that would increase the pore water pressure and reduce the shear strength of the soil, promoting slow-moving landslides. The geological and geotechnical characteristics of the soils suggests that slow-moving landslides would be extended to a large part of the Southern Andes. Finally, this study contributes to improving hazard assessment to mitigate the impact of landslides on the population, infrastructures and natural resources in the Southern Andes.

Loess landslides represent a prevalent and severe type of geological disaster in the Loess Plateau and its surrounding regions. Their frequency and intensity are notably exacerbated under rainfall conditions. This study focuses on investigating the destabilization mechanism of loess landslides induced by rainfall preferential infiltration on the northern slope of the Xining Haihu Bridge. A combination of on-site monitoring, soil property testing, and numerical simulations was employed. The findings reveal that during rainfall events, water rapidly infiltrates into the deep soil layer through pre-existing preferential pathways, such as cracks. This alters the internal water distribution within the soil, leading to localized slope saturation. The subsequent increase in pore water pressure and substantial reduction in soil shear strength emerge as critical factors in triggering loess landslides. Additionally, numerical simulation models were utilized to analyze slope stability under varying rainfall scenarios. The analysis identifies key factors influencing the stability of loess landslides, namely rainfall intensity, duration, and the position and depth of cracks. Furthermore, this study innovatively integrates quantitative analysis of rainfall-induced preferential infiltration with dynamic simulations of landslide stability. This approach offers a more robust theoretical foundation for predicting, assessing, and mitigating loess landslides. By quantifying the relationship between landslide stability and rainfall infiltration patterns, the study provides vital technical support for early warning systems, disaster prevention strategies, and the optimization of engineering measures aimed at addressing loess landslide hazards.

Shallow landslides are often unpredictable and seriously threaten surrounding infrastructure and the ecological environment. Traditional landslide prediction methods are time-consuming, labor-intensive, and inaccurate. Thus, there is an urgent need to enhance predictive techniques. To accurately predict the runout distance of shallow landslides, this study focuses on a shallow soil landslide in Tongnan District, Chongqing Municipality. We employ a genetic algorithm (GA) to identify the most hazardous sliding surface through multi-iteration optimization. We discretize the landslide body into slice units using the dynamic slicing method (DSM) to estimate the runout distance. The model's effectiveness is evaluated based on the relative errors between predicted and actual values, exploring the effects of soil moisture content and slice number on the kinematic model. The results show that under saturated soil conditions, the GA-identified hazardous sliding surface closely matches the actual surface, with a stability coefficient of 0.9888. As the number of slices increases, velocity fluctuations within the slices become more evident. With 100 slices, the predicted movement time of the Tongnan landslide is 12 s, and the runout distance is 5.91 m, with a relative error of about 7.45%, indicating the model's reliability. The GA-DSM method proposed in this study improves the accuracy of landslide runout prediction. It supports the setting of appropriate safety distances and the implementation of preventive engineering measures, such as the construction of retaining walls or drainage systems, to minimize the damage caused by landslides. Moreover, the method provides a comprehensive technical framework for monitoring and early warning of similar geological hazards. It can be extended and optimized for all types of landslides under different terrain and geological conditions. It also promotes landslide prediction theory, which is of high application value and significance for practical use.