Search Results (706)

Bench slopes in open-pit mines are highly susceptible to progressive deformation and instability due to the coupled effects of excavation disturbance, rock mass weathering, and extreme rainfall, posing significant challenges to rapid risk assessment and engineering decision-making. To address the limitations of conventional methods in efficiency and adaptability under complex multi-factor conditions, this study proposes a hybrid simulation–artificial intelligence framework for rapid slope stability assessment and bench face angle optimization. Multi-scenario numerical simulations were conducted by integrating geological investigation data, laboratory and in situ mechanical parameters, and extreme rainfall conditions to characterize slope deformation and failure mechanisms and generate a dataset for machine learning model training. Machine learning models were trained using slope height, bench face angle, unit weight, cohesion, and friction angle as inputs, and safety factors under natural and extreme rainfall conditions as outputs, with hyperparameters optimized by Bayesian optimization. The results indicate that highly weathered rock masses dominate shallow deformation and act as critical weak zones, while extreme rainfall significantly accelerates instability evolution and reduces slope safety factors. Among the RF, SVR, and ELM models, the Bayesian-optimized support vector regression (BO-SVR) exhibits the best predictive performance (R² > 0.98). SHapley Additive exPlanations (SHAP) analysis reveals that slope height and shear strength parameters are the dominant controlling factors, whereas unit weight has a relatively limited influence. Validation using real landslide cases shows good agreement with numerical simulations, confirming the reliability of the proposed framework. The developed approach enables rapid risk evaluation and supports bench face angle optimization, providing an effective tool for intelligent slope management in open-pit mining. Full article

Although deep learning has been successfully used to detect landslide hazards in recent years, existing methods still face challenges due to the variety of landslide characteristics in different terrains and topographies. This study proposes a new framework for landslide detection by comparing various YOLO models. It employs deformable convolutional modules combined with GhostConv modules to enhance feature extraction for landslide targets. The framework uses a structured IoU loss function to optimize the alignment of actual and predicted frames in a directional sense. Additionally, it introduces the CoordAtt attention mechanism to accelerate model convergence and improve training efficiency. The experimental results demonstrate that the enhanced YOLO model (CDCS-YOLO), incorporating four key enhancement modules (Coordinate Attention, Deformable Convolutional Networks, the C3 Module/CSP Architecture and SIoU Loss), achieved a maximum mAP of 96.6%, an accuracy of 96.1%, and a frame rate of 142.6 FPS. Notably, it performed exceptionally well in soil landslide detection, achieving an average detection accuracy surpassing 90%. Based on the experimental results, we explored a morphological landslide classification method further as well as a multi-source differential monitoring strategy integrating UAV imagery, field surveys, ground-based LiDAR data, rainfall information and deformation indicators. The proposed method outperforms the baseline approach and is a promising solution for detecting landslides and geological hazards in Xinjiang. Full article

To investigate the mechanical characteristics of gravel-block soils in the cold regions, four large direct shear tests were designed under different coarse particle contents and three dry density conditions. The stress variations during shearing and the particle fragmentation rate after shearing were measured. The experimental results indicate that when p₅ (the proportion of particles larger than 5 mm) ≥ 40%, the samples exhibit strain hardening behavior, and the stress–strain curve does not exhibit a peak within the range of the tests. The rock fragment skeleton exhibits excellent deformation resistance. With increasing coarse particle content, the internal friction angle of the soil initially decreases and then increases, while the cohesion initially decreases and then increases. Moreover, with increasing initial dry density, both the cohesion and internal friction angle of the gravel-block soils gradually increase. The fractal dimension increases with the increase in the particle fragmentation rate, indicating that the fractal dimension can also represent the degree of particle fragmentation in the soil. The relative fractal dimension increases exponentially with the increase in coarse particle content, indicating that the coarse particle content has a significant impact on the degree of particle fragmentation of gravel-block soils. The higher the coarse particle content, the greater the degree of particle fragmentation of gravel-block soils. When the coarse particle content increases from 0% to 60%, the fractal dimension decreases from 2.825 to 2.555, and the shear strength of the gravel-block soils continuously improves. During the shear process, the gravel-block soils transition from poor grading to well grading, with coarse particles breaking and fine particles filling the gaps between the coarse particles, resulting in a reduction in soil porosity and an increase in particle fragmentation rate and fractal dimension. The research outcomes of this experimental study provide guidance for the study of debris-covered slope landslides in cold regions. Full article

Investigating slope deformation in densely vegetated or remote areas is a major challenge for slope stability assessment. This study introduces and validates an integrated UAV-borne low-frequency Ground Penetrating Radar (UAV-GPR) and LiDAR methodology to characterize an unstable slope in Melizzano, Southern Italy. Radar data were acquired along an east–west transect at ~1 m above ground level, while high-resolution LiDAR were used to generate a detailed Digital Terrain Model for topographic correction and geomorphological analysis. The processed radargram images subsurface features down to ~15 m, revealing a laterally continuous high-amplitude reflector at ~10 m, interpreted as a key main sliding surface. Chaotic reflections above this interface indicate heterogeneous deposits associated with gravitational deformation, while more homogeneous reflections below correspond to stable geological units. The geometry of the reflector suggests a compound landslide mechanism. Borehole data validate the geophysical interpretation, showing depth discrepancies lower than 2 m. The integration of UAV-GPR and LiDAR enables a reliable correlation between surface morphology and subsurface structures. This non-invasive, spatially continuous approach provides an effective framework for subsurface characterization and for improving the interpretation of landslide geometry and internal structure in challenging environments. This study demonstrates the capability of low-frequency UAV-borne GPR to detect deep-seated sliding surfaces (>10 m) in vegetated environments when integrated with high-resolution LiDAR topography. Full article

Geological hazards induced by large-scale and high-intensity mining activities worldwide are primary drivers of regional ecological degradation and pose significant threats to human safety and property. To construct efficient monitoring systems and enhance early warning capabilities, it is essential to clarify the formation mechanisms of various hazards and the suitability of corresponding technologies. Focusing on five typical geological hazards prevalent in mining areas (surface subsidence, ground fissures, landslides, collapses, and sinkholes), this paper characterizes their specific features and monitoring requirements. It systematically analyzes the physical principles, accuracy levels, and technical advantages and limitations of ground-based, aerial, and spaceborne monitoring, as well as multi-source remote sensing data fusion and emerging technologies (e.g., distributed optical fiber, light detection and range, microseismical monitoring, and deep learning). Utilizing case studies from an open-pit coal mine in Turkey and a loess gully mining area in China, the paper evaluates the effectiveness of methods like multi-temporal InSAR and UAV photogrammetry in identifying the evolution of these hazards. The findings indicate that the technological framework for mining area monitoring is transitioning from single-method approaches to integrated systems. However, given the complex mining environment, several bottleneck challenges remain, including single data dimensions, the limited environmental adaptability of aerospace remote sensing, insufficient stability of deep monitoring equipment, and weak anti-interference capabilities under extreme operating conditions. Consequently, this paper proposes that future innovations in geological hazard monitoring in mining areas will focus on multi-platform hierarchical collaboration, the development of multi-parameter fusion early warning criteria, and the construction of digital and visual platforms. Constructing a comprehensive monitoring system characterized by multi-scale collaboration and dynamic prediction capabilities is vital for improving safety standards in mining areas and achieving coordinated development between resource exploitation and environmental protection. The findings provide a theoretical foundation for the precise prevention and control of mining hazards, as well as for land ecological restoration. Full article

Glacial lake outburst floods (GLOFs) are high-impact hazards in mountain regions, yet many events remain poorly documented because field access is limited and lake evolution can occur on sub-weekly time scales. Here, we used high spatiotemporal resolution PlanetScope imagery (3 m) to quantify the seasonal evolution and abrupt drainage of a moraine-dammed glacial lake in August 2025 in northern Pakistan. Historical lake dynamics were reconstructed using PlanetScope (2016–2024) imagery and multi-decadal Landsat observations (1992–2018). Climatic conditions were evaluated using ERA5-Land temperature data, and seasonal snow dynamics were characterized using MODIS and PlanetScope-based snow cover analyses. Multi-decadal satellite imagery indicates that lake formation in this catchment was historically intermittent, with no evidence of abrupt drainage before 2025, highlighting the anomalous nature of the event. PlanetScope observations show steady lake expansion throughout summer 2025, reaching a maximum area of 0.052 km² prior to the GLOF on August 22. Pre- and post-event imagery reveals no discernible landslide or impact trigger. Instead, the observations are most consistent with a failure mechanism driven by meltwater-driven lake growth and overtopping or erosion of the moraine dam. The 2025 summer season (June to September) was characterized by exceptionally warm conditions and unprecedented early snow depletion relative to the 2000–2024 baseline, suggesting a strong climatic and cryospheric contribution to the outburst. These results demonstrate the value of integrating dense time series of satellite observations and climatic data for capturing glacial-lake life cycles and diagnosing likely controls on outburst initiation. The study highlights the critical role of high-frequency satellite remote sensing for improving GLOF monitoring and early-warning capabilities in data-scarce mountain environments. Full article

Rapid landslide susceptibility screening is important for linear engineering projects because long corridors, numerous slope units, limited data, and tight schedules often restrict the use of data-intensive models. This study develops an engineering-oriented reduced-factor screening framework based on the Information Value (IV) model and applies the framework to the Beijing-Zhangjiakou Railway corridor. A conventional 10-factor IV model was first established as the reference model. Reduced-factor models were then screened under the same study area, the same landslide inventory, the same modelling workflow, and the same factor classification scheme. The 10-factor model reached an accuracy of 94.87%. Two reduced five-factor models reached the same accuracy: Slope + Aspect + Elevation + Lithology and Engineering Rock + NDVI, and Slope + Aspect + Elevation + Lithology and Engineering Rock + Distance to Rivers. The comparison shows that the full-factor model can be simplified without loss of validation accuracy when a stable terrain–geological framework is retained and a suitable external factor is added. Because the available inventory contains only 45 landslides and does not distinguish failure mechanisms consistently, the proposed model should be regarded as a preliminary probabilistic screening tool rather than a mechanism-specific prediction model. The proposed framework provides a practical approach for corridor-scale hazard screening under incomplete data conditions. Full article

This study explores the environmental justice issues associated with landfill compensation policies in Bantargebang, Indonesia. Although compensation programs have been implemented for many years, communities living near landfills continue to experience ongoing environmental damage and significant health concerns. Using a qualitative descriptive method, this research explores systemic barriers through in-depth interviews, observations, and water quality analysis. The findings indicate that labeling the program as “Social Assistance” within the Local Government Information System (SIPD) redefines ecological compensation as a fixed form of charity, rather than as a mechanism for genuine environmental restitution. Laboratory data show severe bacteriological contamination, with Total Coliform levels reaching 95%, forcing residents to bear substantial “hidden costs” for clean water, perpetuating a cycle of financial dependence. The growing normalization of health hazards is evident in over 5000 annual cases of acute respiratory infections, and the deadly landslide in March 2026, in which claimed seven lives and injured six others. These incidents underscore the failure of existing remediation approaches to safeguard human dignity and well-being. To address these shortcomings, this study proposes the adoption of an Integrated Compensation Model based on Green Social Work. This model emphasizes structural investment, spatial risk-based indices using quantitative data, and budget coding adjustments within the SIPD. This approach highlights the urgent need to move beyond temporary charitable assistance and instead pursue meaningful environmental justice, while positioning social workers as “Social-Ecological Brokers” who help restore dignity and well-being in communities often treated as “sacrifice zones.” Full article

At around 8:40 a.m. on 17 July 2024, the Wanshuitian landslide in the Three Gorges Reservoir Area (TGRA) experienced a deformation failure characterized by thrust load-caused deformations and high-speed sliding. Using geological surveys and unmanned aerial vehicle (UAV) photography, this study divided the Wanshuitian landslide area into five zones: sliding initiation (A1), secondary disintegration (A2), main accumulation (B1), right falling (B2), and left falling (B3) zones. Through monitoring data analysis and GeoStudio-based numerical simulations, this study revealed the mechanisms behind the landslide failure mode characterized by slope sliding approximately along the strike of the rock formation under the coupling effect of hydrological and geological conditions. The results indicate that factors inducing the landslide failure include the geomorphic feature of alternating grooves and ridges, the lithologic assemblage characterized by interbeds of soft and hard rocks, the slope structure with well-developed joints, and the sustained heavy rains in the preceding period. In the Wanshuitian landslide area, mudstone valleys are prone to accumulate rainwater, which can infiltrate directly into the weak interlayers of rock masses and soften the rock masses. Multi-peak rain events with a short time interval serve as a critical factor in groundwater recharge. Within 17 days preceding its failure, the Wanshuitian landslide experienced a superimposed process of heavy and secondary rain events with a short interval (four days). Rainwater from the first heavy rain event failed to completely discharge during the short interval, while the secondary rain event also caused rainwater accumulation. These led to a continuous rise in the groundwater table, a constant decrease in the shear strength of the slope, and ultimately the landslide instability. Since the landslide sliding in the dip direction of the rock formation was impeded, the main sliding direction of the landslide formed an angle of 88° with this direction. This led to a unique failure mode characterized by slope sliding approximately along the strike of the rock formation. Based on these findings, this study proposed characteristics for the early identification of the failure of similar landslides, aiming to provide a robust scientific basis for the monitoring, early warning, and prevention and control of the failure of similar landslides. Full article

The growing complexity of human systems and the increasing frequency of climate-driven hazards have transformed some disasters from isolated events into cascading phenomena which propagate through critical infrastructure networks, disrupting essential services and amplifying systemic risk. This work examines the impacts of extreme storms and subsequent flooding on critical entities as defined under the new EU Directive (Critical Entities Resilience, CER). This study introduces a structured Critical Entities Disruption Database—Greece (CEDD-GR), as a methodological framework for systematically recording and analysing disruptions to critical entities, and applies it to the case of Storm Daniel (2023), one of the most severe flood events recorded in Greece. The analysis identified direct impacts across eight of the eleven sectors defined in the CER Directive, namely, energy, transport, health, drinking water, wastewater, public administration, digital infrastructure and food production, processing and distribution. A total of 21 different types of critical entities were documented, revealing the mechanisms through which failures affected different subsectors. The results underscore the systemic fragility of critical entities when exposed to extreme storms, compound flooding, and mass wasting processes (landslides, ground subsidence) and highlight the need for integrated resilience planning in line with the CER framework. Full article

The geological environment of southeastern Tibet is characterized by complex tectonics and high climatic sensitivity, and giant accumulation landslides pose significant threats to infrastructure and human safety. This study investigates the Pangcun giant accumulation landslide using SBAS-InSAR (2017–2024), UAV photogrammetry, field investigations, and wavelet coherence analysis to examine its deformation and driving mechanisms. The landslide exhibits continuous, slow deformation with clear spatial heterogeneity, divided into two zones, with the largest displacement occurring in the middle of Zone B. Field evidence is consistent with the InSAR results. Wavelet coherence analysis reveals a lagged response of displacement to precipitation at a timescale of about three months. The landslide’s evolution is controlled by unfavorable topography and fragmented materials, with precipitation as the primary trigger. Human activities (agricultural irrigation and slope-toe road excavation) and seismic disturbances also contribute to its progressive development. Full article

The Dongchuan District in Kunming City is located in the transition zone between the Yunnan–Guizhou Plateau and the Sichuan Basin. As a region with a copper mining history of over 2000 years, the district has experienced frequent landslides that pose serious threats to human lives, property, and ecological sustainability. Therefore, it is essential to compile a comprehensive landslide inventory and analyze the relationships between landslide spatial distribution and influencing factors for geological hazard prevention. High-resolution remote sensing imagery was interpreted to establish a landslide inventory, based on which the spatial distribution and geometric characteristics of landslides were systematically analyzed. The results show that a total of 1623 landslides were identified, with a total area of 10.36 km². Landslides predominantly occur at elevations of 1000–2000 m, on slopes of 20–45°, with aspects of 255–285°, and relief between 150 and 400 m, in areas with annual rainfall below 825 mm, within 1000 m of rivers and 3000 m of fault lines, and 1000–5000 m of mines. Four landslide clusters were delineated along the Xiao River Fault, highlighting the significant influence of the fault on the spatial distribution of landslides. Most landslides are longitudinal in planform, with travel distances (L) of 50–450 m and heights (H) from 25 to 350 m, both exhibiting allometric scaling with volume. The mean H/L ratio is 0.56 (corresponding to a mean reach angle of 29°), significantly higher than that in Baoshan City (21°). The results provide insights into landslide initiation mechanisms and spatial distribution patterns on the northern margin of the Yunnan–Guizhou Plateau, offering valuable data for landslide hazard assessment and sustainable regional development. Full article

Time-series InSAR is an important means for early identification and monitoring of landslides. However, in complex mountainous areas, it still faces challenges such as significant geometric distortions and complicated deformation mechanisms. To address these issues, this paper proposes a landslide identification and prediction framework that integrates ascending and descending orbits InSAR observations with physics-guided deep learning. Taking Yangbi County, Yunnan Province, as a case study, we combined ascending and descending Sentinel-1A data and employed the SBAS-InSAR method to identify potential landslides, detecting a total of 41 hazardous sites. The cumulative displacement time series of typical landslides were further extracted along the slope aspect to more realistically reflect landslide movement characteristics. On this basis, wavelet decomposition was introduced to separate the displacement series into trend and periodic components. Gray relational analysis was then used to select influencing factors such as precipitation and temperature, and a stepwise prediction model based on LSTM (WT-LSTM) was constructed. The results indicate that the model achieves significantly higher prediction accuracy at characteristic points of the representative landslide (RMSE = 1.16–2.19 mm) compared to standalone LSTM and SVR models. These findings demonstrate its effectiveness and potential applicability in landslide deformation monitoring and prediction in complex mountainous areas, while also providing a useful reference for landslide risk early warning. Full article

This paper investigates the mechanical transfer mechanism of bimetallic composite pipes used in highly sour gas fields located in high-steep mountainous areas. It systematically analyzes the mechanical response behavior of these pipes under the coupled effects of complex geological conditions and operational loads. By establishing and validating a finite element model that accounts for material nonlinearity and pipe–soil interaction, the study examines the influence of key factors—including internal pressure, landslide displacement, and base pipe wall thickness—on the stress distribution and transfer mechanism within the pipeline. The results demonstrate that increased internal pressure significantly elevates both circumferential and axial stresses: when internal pressure increases from 7 MPa to 9 MPa, the liner hoop stress increases by 35.5% and the base pipe axial stress increases by 27.5%. When landslide displacement exceeds a critical threshold of 3 m, the stress in the base pipe rises sharply, with axial stress increasing by 39.7% when displacement increases from 3 m to 5 m; conversely, increasing the base pipe wall thickness from 12 mm to 15 mm effectively reduces the overall stress level, decreasing base pipe axial stress by 40.4% and liner axial stress by 86.9%. Stress transfer exhibits a dual-path characteristic, which can be described as “bidirectional transfer induced by internal pressure” and “progressive transfer caused by landslide”. These quantitative findings provide a theoretical basis for the safe design and operation of bimetallic composite pipes in high-steep mountainous regions. Full article

Hydrodynamically triggered landslides remain a major concern in reservoir regions, where the mechanisms controlling displacement evolution are still not fully understood and the multi-scale deformation responses induced by individual hydrodynamic factors remain difficult to quantify. To address these issues, this study establishes a TSD-TET composite framework by integrating time-series signal decomposition with deep learning for multi-scale displacement prediction and the mechanism-oriented interpretation of hydrodynamically triggered landslides. The monitored displacement sequence is first decomposed into physically interpretable components, including trend, periodic, and random terms. Each component is subsequently predicted using deep temporal learning models to capture different deformation characteristics at multiple temporal scales. Meanwhile, key hydrodynamic driving factors, including rainfall, reservoir water level, and groundwater level, are decomposed within the same framework to examine their statistical associations with different displacement components. The proposed approach is applied to the Donglingxin landslide located in the Sanbanxi Hydropower Station reservoir area. Results show that the model achieves high prediction accuracy under both long-term forecasting horizons and limited-sample conditions, with a cumulative displacement coefficient of determination reaching R² = 0.945. Mechanism analysis further indicates that trend deformation is mainly controlled by geological structure and gravitational loading, periodic deformation is strongly modulated by hydrological cycles associated with reservoir water level fluctuations, and random deformation is more likely to reflect short-term disturbances and transient hydrodynamic forcing. These findings provide new insights into the deformation mechanisms of hydrodynamically triggered landslides and offer a promising technical pathway for improving displacement prediction, monitoring, and early warning of reservoir-induced landslide hazards. Full article