Search Results (284)

Open-pit mining often exposes weak rock layers, the strength of which significantly affects the stability of slopes. If these rock layers are also prone to disintegration and expansion, cyclic rainfall can exacerbate instability. Rainfall-induced changes in the seepage field also indirectly threaten the stability of slopes. Therefore, investigating the characteristics of mudstone limestone and the impact of the seepage field on slope instability under different wet–dry cycles is of great significance for the safe mining of open-pit mines. This paper takes the mudstone limestone slope of a certain open-pit mine in the southwest as the starting point and conducts experiments on saturated density, water absorption rate, permeability coefficient, compressive strength, and variable angle shear strength. Combined with scanning electron microscopy and phase analysis of X-ray diffraction analysis, the macroscopic and microscopic characteristics of the samples are comprehensively analyzed. FLAC3D software is used to explore the changes in the seepage field and the mechanism of instability. Our research found that for the preparation of mudstone limestone samples, a particle size of less than 1 mm and a drying temperature of 50 °C are optimal, with specific values for initial natural and saturated density, and natural water content. As the number of wet–dry cycles increases, the saturated density of mudstone limestone increases; the water absorption rate first rises sharply and then rises slowly; the permeability coefficient first rises sharply and then stabilizes, finally dropping sharply; the compressive and shear strength decreases slowly, and the internal friction angle changes little; frequent cycles also lead to mudification and seepage filtration. At the microscopic level, pores become larger and more regular, and the distribution is more concentrated; changes in mineral content weaken the strength. Combined with numerical simulation, the changes in the seepage field at the bottom of the slope exceed those at the slope surface and top, the transient saturated area expands, and the overall and local slope stability coefficients gradually decrease. During the third cycle, the local stability is lower than the overall stability, and the landslide trend shifts. In conclusion, wet–dry cycles change the pores and mineral content, affecting the physical and mechanical properties, leading to the deterioration of the transient saturated area, a decrease in matrix suction, and an increase in surface gravity, eventually causing slope instability. Full article

In light of the increasing frequency of extreme rainfall events, there has been a concomitant rise in landslides triggered by such precipitation. Despite the extensive research conducted on rainfall-induced landslides, the practical implementation of these findings is constrained by geological and environmental factors. Notably, there is a paucity of research on rainfall-induced shallow landslides in Hubei Province, China. Therefore, this study analyzes the fundamental characteristics and rainfall characteristics of landslides induced by multiple rounds of extreme rainfall in Qichun County in June and July 2016. The study explores the influence of five variables—namely, altitude, slope, slope aspect, stratum lithology, and rainfall—on landslides. The study uses numerical analysis to reveal the initiation mechanism of landslides. The research conclusions are as follows: The landslides within the study area are closely related to its natural topography, stratum lithology, and human activities. The majority of landslides are triggered by short-term extreme rainfall, while a smaller number are related to long-term continuous rainfall. The formation mechanism of landslides is primarily driven by dynamic water seepage, and the destruction process often lags behind the rainfall process. The conclusions can provide theoretical guidance for risk prevention and early warning of rainfall-induced landslides in the region. Full article

In landslide susceptibility modeling, research has predominantly focused on predicting landslides by identifying predisposing factors, often using inventories primarily based on the highest points of landslide crowns. However, a significant challenge arises when the transported mass impacts human activities directly, typically occurring in the deposition areas of these phenomena. Therefore, identifying the terrain characteristics that facilitate the transport and deposition of displaced material in affected areas is equally crucial. This study aimed to evaluate the predictive capability of identifying where displaced material might be deposited by using different inventories of specific parts of a landslide, including the source area, intermediate area, and deposition area. A sample segmentation was conducted that included inventories of these distinct parts of the landslide in the hydrographic basin of Lake Ilopango, which experienced debris flows and debris floods triggered by heavy rainfall from Hurricane Ida in November 2009. Given the extensive variables extracted for this evaluation (20 variables), the Induced Smoothed (IS) version of the Least Absolute Shrinkage and Selection Operator (LASSO) methodology was employed to determine the significance of each variable within the datasets. Additionally, the Multivariate Adaptive Regression Splines (MARS) algorithm was used for modeling. Our findings revealed that models developed using the deposition area dataset were more effective compared with those based on the source area dataset. Furthermore, the accuracy of models using deposition area data surpassed that of that using data from both the source and intermediate areas. Full article

Debris flows are rapid, destructive landslides that pose significant risks in mountainous regions. This study presents a novel algorithm to simulate debris flow dynamics, focusing on sediment transport from 0-order basins to depositional zones. The algorithm integrates the D8 flow direction method with an adjustable friction coefficient to enhance the accuracy of debris flow trajectory and deposition modeling. Its performance was evaluated on three real-world cases in the Otoishi River basin, affected by rainfall-induced debris flows in July 2017, and the Aso Bridge landslide triggered by the 2016 Kumamoto Earthquake. By utilizing diverse friction coefficients, the study effectively captured variations in debris flow behavior, transitioning from fluid-like to more viscous states. Simulation results demonstrated a precision of 88.9% in predicting debris flow paths and deposition areas, emphasizing the pivotal role of the friction coefficient in regulating mass movement dynamics. Additionally, Monte Carlo (MC) simulations enhanced the identification of critical slip surfaces within 0-order basins, increasing the accuracy of debris flow source detection. This research offers valuable insights into debris flow hazards and risk mitigation strategies. The algorithm’s proven effectiveness in simulating real-world scenarios highlights its potential for integration into disaster risk assessment and prevention frameworks. By providing a reliable tool for hazard identification and prediction, this study supports proactive disaster management and aligns with the goals of sustainable development in regions prone to debris flow disasters. Full article

Landslide susceptibility evaluation is an indispensable part of disaster prevention and mitigation work. Selecting effective evaluation methods and models for landslide susceptibility assessment is of significant importance. This study focuses on selected areas in Yunyang County, Chongqing City. By interpreting high-resolution satellite remote sensing images from before and after heavy rainfall on 31 August 2014, the distribution of rainfall-induced accumulation landslides was obtained. To evaluate the susceptibility of accumulation landslides, we have equated evaluation factors to accumulation distribution prediction factors. Eight evaluation factors were extracted using multi-source data, including lithology, elevation, slope, remote sensing image texture features, and the normalized difference vegetation index (NDVI). Various machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), and BP Neural Network models, were employed to assess the susceptibility of rainfall-induced accumulation landslides in the study area. Subsequently, the accuracy of the evaluation models was compared and verified using the Receiver Operating Characteristic (ROC) curve, and the evaluation results were analyzed. Finally, the developed Random Forest model was applied to Gongping Town in Fengjie County to verify its applicability in other regions. The findings indicate that the complex geological conditions and the unique tectonic erosion landform patterns in the northeastern region of Chongqing not only make this area a center of heavy rainfall but also lead to frequent and recurrent rainfall-induced landslides. The Random Forest model effectively reflects the development characteristics of accumulation landslides in the study area. High and very high susceptibility zones are concentrated in the northern and central regions of the study area, while low and moderate susceptibility zones predominantly occupy the mountainous and riverside areas. Landslide susceptibility mapping in the study area shows that the Random Forest model yields reasonably graded results. Elevation, remote sensing image texture features, and lithology are highly significant factors in the evaluation system, indicating that the development factors of slope geological disasters in the study area are mainly related to topography, geomorphology, and lithology. The landslide susceptibility evaluation results in Gongping Town, Fengjie County, validate the applicability of the Random Forest model developed in this study to other regions. Full article

Taiwan is located at a junction of tectonic plates, which results in frequent earthquakes. Its terrain is mostly hilly, and its rainfall ranks among the highest in the world. Each of these elements affects the stability of slopes in various regions of Taiwan. Several slopes along Taiwan’s Freeway 1 and 5 have experienced landslides and rockfalls. It is imperative that the slope stability of these national freeways be analyzed to avoid future slope collapses brought on by precipitation or other outside factors. Thus, three sites on Taiwan’s Freeway 1 and 5 were chosen for numerical slope stability analysis in this study. PLAXIS 2D CE (Version: 24.02.00.1144) finite element software was used in this study to simulate and analyze the safety of freeway slope protection projects. Displacements induced by normal and high groundwater levels were discussed. Moreover, a pseudo-static study of slope displacements under seismic conditions was performed. According to the results of the numerical study, the force operating on the slope was centered on the sliding surface when the groundwater level was normal, and it extended to the top when the groundwater level was high. By comparison, under seismic conditions, the force acting on the slope extended to the whole slope. Furthermore, the slope safety factor of Site 1 was greater than the design specification value in three different scenarios. This confirms that the slope protection project at Site 1 is effective. Full article

Watersheds are critical ecosystems that provide essential services, but they face increasing threats from deforestation, land use changes, and climate variability. The Mata Allo Sub-Watershed, which is characterized by steep topography and high rainfall, is particularly vulnerable to erosion, landslides, and habitat loss, necessitating robust conservation strategies. This study used principal component analysis (PCA) to assess ecological sensitivity, focusing on slope, rainfall, vegetation density, and land cover. The PCA results identified land cover as the most influential positive factor in F1 (loading value: 0.588), increasing sensitivity due to human-induced land use changes, while rainfall contributed most negatively (−0.638) by potentially mitigating extreme ecological risks. These contrasting roles underscore the complexity of interactions shaping watershed sensitivity. Slope strongly influenced F2 (−0.795), explaining 26.48% of the variance and highlighting the critical role of steep slopes in exacerbating erosion risks. Vegetation density in F3 (−0.679) and rainfall in F4 (−0.724) played significant roles in stabilizing soil and mitigating ecological risks, emphasizing their importance in reducing watershed sensitivity. The “Extremely Sensitive” class covers 48.79% of the watershed, primarily in areas with steep slopes and sparse vegetation, while “High Sensitivity” areas occupy 34.93%. Projections for 2032 suggest a reduction in “Extremely Sensitive” zones to 41.00%, reflecting improvements from targeted management interventions. These findings provide a foundation for promoting sustainable watershed management, enhancing climate resilience, and supporting biodiversity conservation efforts in vulnerable regions. Full article

Rainfall-induced landslides are widely distributed in many countries. Rainfall impacts the hydraulic dynamics of groundwater and, therefore, slope stability. We derive an analytical solution of slope stability considering effective rainfall based on the Richards equation. We define effective rainfall as the total volume of rainfall stored within a given range of the unsaturated zone during rainfall events. The slope stability at the depth of interest is provided as a function of effective rainfall. The validity of analytical solutions of system states related to effective rainfall, for infinite slopes of a granite residual soil, is verified by comparing them with the corresponding numerical solutions. Additionally, three approaches to global sensitivity analysis are used to compute the sensitivity of the slope stability to a variety of factors of interest. These factors are the reciprocal of the air-entry value of the soil α, the thickness of the unsaturated zone L, the cohesion of soil c, the internal friction angle ϕ related to the effective normal stress, the slope angle β, the unit weights of soil particles γ_s, and the saturated hydraulic conductivity K_s. The results show the following: (1) The analytical solutions are accurate in terms of the relative differences between the analytical and the numerical solutions, which are within 5.00% when considering the latter as references. (2) The temporal evolutions of the shear strength of soil can be sequentially characterized as four periods: (i) strength improvement due to the increasing weight of soil caused by rainfall infiltration, (ii) strength reduction controlled by the increasing pore water pressure, (iii) strength reduction due to the effect of hydrostatic pressure in the transient saturation zone, and (iv) stable strength when all the soil is saturated. (3) The large α corresponds to high effective rainfall. (4) The factors ranked in descending order of sensitivity are as follows: α > L > c > β > γ_s > K_s > ϕ. Full article

Landslides induced by heavy rainfall are common in southern China and pose significant risks to the safe operation of transmission lines. To ensure the reliability of transmission line operations, this paper presents a stability prediction model for transmission tower slopes based on the Improved Sand Cat Swarm Optimization (ISCSO) algorithm and Support Vector Machine (SVM). The ISCSO algorithm is enhanced with dynamic reverse learning and triangular wandering strategies, which are then used to optimize the kernel and penalty parameters of the SVM, resulting in the ISCSO-SVM prediction model. In this study, a typical transmission tower slope in southern China is used as a case study, with the transmission tower slope database generated through orthogonal experimental design and Geo-studio simulations. In addition to traditional input features, an additional input—transmission tower catchment area—is incorporated, and the stable state of the transmission tower slope is set as the predicted output. The results demonstrate that the ISCSO-SVM model achieves the highest prediction accuracy, with the smallest errors across all metrics. Specifically, compared to the standard SVM, the MAPE, MAE, and RMSE values are reduced by 70.96%, 71.41%, and 57.37%, respectively. The ISCSO-SVM model effectively predicts the stability of transmission tower slopes, thereby ensuring the safe operation of transmission lines. Full article

Landslides on the Jiaxi Highway in Qinghai Province threaten construction safety and quality. The on-site data analysis shows that excavation at the foot of the slope and heavy rainfall are the key factors causing the displacement of the Q1 monitoring point by 1825 mm. This article uses numerical simulation methods combined with the strength reduction method to study the stability changes of slopes under different working conditions. Numerical simulations identified the landslide location and predicted a 1960 mm slip and a safety factor of 1.26 under natural conditions, indicating risks. The study adopted a strategy combining slope cutting, load reduction, and sheet pile wall reinforcement. After the first treatment, the safety factor rose to 1.83 with a 40 mm displacement; after the second, it reached 2.36 with a 37 mm displacement. Continuous monitoring showed a 50 mm displacement over six months, indicating stability. Rainfall simulations before and after treatment explained the stability evolution and local slope stability. Treatments increased the safety factor to 2.16 with a 17.6 mm displacement. This study significantly improved highway landslide stability and verified treatment effectiveness, providing a reference for similar geological conditions. Full article

Rainfall infiltration is the primary triggering factor for the instability of unsaturated slopes. At present, rainfall-induced landslides are mainly considered to be influenced by the overall infiltration conditions, while few investigations have been conducted on the influence of infiltration boundaries on slope instability. This study proposes a rainfall infiltration method using a discrete element model (DEM), which is based on saturated–unsaturated seepage theory. The influence of three infiltration boundaries on the instability of homogeneous unsaturated soil slopes was studied. The results showed that the infiltration rate of a rainfall-covered slope crest was faster than that of rainfall-covered slope surfaces. A transient saturated zone was formed on the slope surface after a certain duration of rainfall. Rain infiltration boundary conditions significantly impact the saturation distribution, seepage field, failure mode, and failure period. The safety and stability factors for the rainfall-covered slope crest and full area decreased monotonically with the increase in rainfall duration, while there was a brief increase at the initial stage of rainfall before a quick decline for rainfall-covered slope surfaces. This research provides a preliminary exploration of the impact of rainfall boundary conditions on the instability of slopes, offering a reference basis for DEM simulations that consider slope stability under the influence of rainfall infiltration. Full article

Understanding the factors that contribute to slope failures, such as soil saturation, is essential for mitigating rainfall-induced landslides. Cost-effective capacitive soil moisture sensors have the potential to be widely implemented across multiple sites for landslide early warning systems. However, these sensors need to be calibrated for specific applications to ensure high accuracy in readings. In this study, a soil-specific calibration was performed in a laboratory setting to integrate the soil moisture sensor with an automatic monitoring system using the Internet of Things (IoT). This research aims to evaluate a low-cost soil moisture sensor (SKU:SEN0193) and develop calibration equations for the purpose of slope model experiment under artificial rainfall condition using silica sand. The results indicate that a polynomial function is the best fit, with a coefficient of determination (R²) ranging from 0.918 to 0.983 and a root mean square error (RMSE) ranging from 1.171 to 2.488. The calibration equation was validated through slope model experiments, with soil samples taken from the models after the experiment finished. Overall, the moisture content readings from the sensors showed approximately a 12% deviation from the actual moisture content. The findings suggest that the cost-effective capacitive soil moisture sensor has the potential to be used for the development of landslide early warning system. Full article

Rainfall-induced landslides are widespread in Indonesia, particularly in West Java, where volcanic residual soils are typically stable but may become unstable during heavy rainfall. This study aims to back analyze the geotechnical factors contributing to the Cimanggung landslide in 2021. The methods applied in this study include site investigations, laboratory testing, and numerical modeling. We performed deterministic, coupled seepage-slope stability analysis and Monte Carlo probabilistic analysis to assess the slope performance prior to and after rainfall infiltration. The results reveal that the initial water level significantly affects slope stability, and heavy rainfall infiltration triggered the landslide’s initiation. The deep water table (over 20 m below ground level) maintains the slope stability, and increasing the water table to 16 m compromises its stability. Heavy rainfall infiltration reduces suction in the unsaturated zone, decreasing the shear strength and triggering landslides. The heavy rainfall infiltration did not penetrate deep enough to raise the water table; rather, poor urban drainage on the upper slope caused it. Rainfall infiltration caused wetting in the upper zone, weakening the slope and causing loss of support. It is recommended that effective drainage management and integrated slope monitoring be applied to mitigate landslide risks in this region. Full article

Hurricane Helene triggered 1792 landslides across western North Carolina and has caused damage to 79 bridges to date. Helene hit western North Carolina days after a low-pressure system dropped up to 254 mm of rain in some locations of western North Carolina (e.g., Asheville Regional Airport). The already waterlogged region experienced devastation as significant additional rainfall occurred during Helene, where some areas, like Asheville, North Carolina received an additional 356 mm of rain (National Weather Service, 2024). In this study, machine learning (ML)-generated multi-hazard landslide susceptibility maps are compared to the documented landslides from Helene. The landslide models use the North Carolina landslide database, soil survey, rainfall, USGS digital elevation model (DEM), and distance to rivers to create the landslide variables. From the DEM, aspect factors and slope are computed. Because recent research in western North Carolina suggests fault movement is destabilizing slopes, distance to fault was also incorporated as a predictor variable. Finally, soil types were used as a wildfire predictor variable. In total, 4794 landslides were used for model training. Random Forest and logistic regression machine learning algorithms were used to develop the landslide susceptibility map. Furthermore, landslide susceptibility was also examined with and without consideration of wildfires. Ultimately, this study indicates heavy rainfall and debris-laden floodwaters were critical in triggering both landslides and scour, posing a dual threat to bridge stability. Field investigations from Hurricane Helene revealed that bridge damage was concentrated at bridge abutments, with scour and sediment deposition exacerbating structural vulnerability. We evaluated the assumed flooding potential (AFP) of damaged bridges in the study area, finding that bridges with lower AFP values were particularly vulnerable to scour and submersion during flood events. Differentiating between landslide-induced and scour-induced damage is essential for accurately assessing risks to infrastructure. The findings emphasize the importance of comprehensive hazard mapping to guide infrastructure resilience planning in mountainous regions. Full article

In 2013, a Ms 6.6 earthquake occurred at the boundary of Min County and Zhang County, triggering numerous landslides. Notably, two landslides with significantly different sliding characteristics emerged less than 100 m apart in Yongguang Village, Min County. The eastern landslide was characterized by instability induced by seismic inertial forces, whereas the western landslide exhibited flow slides triggered by liquefaction in loess. To further analyze the causes of these landslides, this study employed a 1 m depth ground temperature survey to probe the shallow groundwater in the area, aiming to understand the distribution of shallow groundwater. Based on the results from the 1 m depth ground temperature survey, a random forest model was applied to regressively predict the initial groundwater levels. The TRIGRS model was utilized to evaluate the influence of pre-earthquake rainfall conditions on landslide stability, and the pore water pressure outputs from TRIGRS were integrated with the Scoops3D model to analyze landslide stability under seismic effects. The results indicate that the combination of the 1 m depth ground temperature survey with high-density electrical methods and random forest approaches effectively captures the initial groundwater levels across the region. Notably, the heavy rainfall occurring one day prior to the earthquake did not significantly reduce the stability of the landslide in Yongguang Village. Instead, the abundant groundwater in the source area of the western landslide, combined with several months of pre-earthquake rainfall, resulted in elevated groundwater levels that created favorable conditions for its occurrence. While the primary triggering factor for both landslides in Yongguang Village was the earthquake, the distinct topographic and groundwater conditions led to significantly different sliding characteristics under seismic influence at the same slope. Full article