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.

The frequent occurrence of earthquakes worldwide has rendered highway slope protection projects highly vulnerable to damage from seismic events and their secondary disasters. This severely hampers the smooth implementation of post-disaster rescue and recovery efforts. To address this challenge, this study proposes a comprehensive method for assessing seismic losses in slope protection projects, incorporating factors such as topography and elevation to enhance its universality. The method categorizes seismic losses into two main components: damage to protection structures and costs associated with landslide and rockfall clearance and transportation. This study estimates the cost range for common protection structures and clearance methods under general conditions based on widely recognized quota data in China. It establishes criteria for classifying the damage states of protection structures and provides loss ratio values based on real-world seismic examples and expert experience, constructing a model for assessing damage losses. Additionally, by summarizing the geometric characteristics of soil and rock accumulations on road surfaces, a method for estimating landslide volumes is proposed, considering the dynamic impact of slope gradients on clearance and transportation volumes, and a corresponding cost assessment model for clearance and transportation is developed. The feasibility and reliability of the proposed method are verified through two case studies. The results demonstrate that the method is easy to implement and provides a scientific basis for improving relevant standards and practices. It also offers an efficient and scientific tool for loss assessment to industry practitioners.

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.

Submarine landslides are a geological hazard that may cause significant damage, and are among the most serious problems in offshore geotechnics. Understanding the mechanism of submarine landslide/offshore structure interaction is essential for risk assessment, but it is challenging due to its complexities. In this study, ten centrifuge tests were conducted to determine how offshore wind turbines founded on four piles respond to consecutive submarine landslides. The tests highlighted two mechanisms of soil deformation and foundation settlement associated with the landslide cycle: (1) deformations of the clay were associated with induced excess pore water pressure, and increased with the number of landslides; and (2) by contrast, foundation settlements largely depended on the dynamic impact of the first cycle and remained unchanged for the remaining events. The settlements were 0.5 m for the 10 m pile foundation and about 0.1 m for the 20 m pile foundation, both in clay and in sand. It was also found that increasing pile length reduces the excess pore water pressure, soil deformation and foundation settlement.

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.

Antislide piles are currently applied widely in slope reinforcement engineering, but investigation of the stability of slopes stabilized with this measure under the action of mainshock-aftershock (Ms-As) sequences is very limited. In this study, the probability density evolution method (PDEM) and the Newmark method is adopted to evaluate the reliability of slope reinforced by antislide piles subjected to Ms-As sequences considering the spatial variability of material parameters. Firstly, stochastic Ms-As sequences are generated by combining a physical function model, the Copula function, and the narrowband harmonic group superposition method. In addition, the spectral representation method is taken to generate the random field and the parameters are assigned to the numerical model. Then, the Newmark method is applied to batch-calculate the permanent displacement (Disp) of the slope caused by the Ms-As sequences. The effects of pile position, pile length, and coefficient of variation of cohesion and friction angle (COVC and COVF) on the average value of Disp are discussed. Finally, based on the PDEM, the seismic reliability of the slope strengthened by antislide piles subjected to the Ms-As sequences are obtained. The research results indicate that with the COV increases, the average value of Disp of the slope shows a gradual tendency to increase, and the average value is more sensitive to COVC. Compared with the unreinforced slope, the Disp of the slope strengthened by antislide piles is reduced. The cumulative damage caused by the aftershock and the risk of failure can be significantly reduced by setting a reasonable antislide pile.

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.

Loess disaster chains on the Heifangtai Platform, China, cause frequent loess landslides and form landslide dams, thus obstructing rivers. In addition, the failure of landslide dams causes loess mudflows and other related disasters. In this study, the influences of different inflow rates on the failure process and triggering mechanisms of loess landslide dams were explored using five sets of model experiments. These experimental results revealed that the failure of loess landslide dams occurs through overtopping and piping failure, or overtopping failure. Overtopping and piping failure can be divided into infiltration, seepage channel development, break overflow, and rebalancing. When the inflow rate was 1.0 L/s, the water could not penetrate the dam in time. Overtopping failure primarily involves horizontal and downward erosion of the breach. The inflow rate was positively correlated with soil transport, peak flow velocity, and peak bulk density based on the experimental data. The bulk density of the failure mudflow was categorized into slow increase, transition, and attenuation stages based on our experimental results. In addition, by analyzing the volume and stability of residual dams, the likelihood and damage degree of secondary hazards after the dam failure were initially explored. This study provides a scientific basis for relevant studies on loess landslide dam failure.

Landslides pose significant risks to human life and infrastructure, particularly in mountainous regions like Inje, South Korea. This study aims to develop detailed landslide susceptibility maps (LSMs) using statistical (i.e., Frequency Ratio (FR), Logistic Regression (LR)) models and a hybrid integrated approach. These models incorporated various factors influencing landslides, including aspect, elevation, rainfall, slope, soil depth, slope length, and landform, derived from comprehensive geospatial datasets. The FR method assesses the likelihood of landslides based on historical occurrences relative to specific factor classes, while the LR method predicts landslide susceptibility through the statistical modeling of multiple predictor variables. The results from the FR, LR, and hybrid methods showed that the cumulative area covered by high and very high landslide susceptibility zones was 13.8%, 13.0%, and 14.28%, respectively. The results were validated using Receiver Operating Characteristic (ROC) curves and the Area Under the Curve (AUC), revealing AUC values of 0.83 for FR, 0.86 for LR, and 0.864 for the hybrid method, indicating high predictive accuracy. Subsequently, we used K-mean clustering algorithms on the hybrid LSI to identify the higher LSI cluster of the region. Furthermore, sensitivity analysis based on landslide density confirmed that all methods accurately identified high-risk areas. The resulting LSMs provide critical insights for land-use planning, infrastructure development, and disaster risk management, enhancing predictive accuracy and aiding in the prevention of future landslide damage.