Search Results (578)

Rapid and accurate detection of landslide-affected areas is critical for disaster response and risk mitigation. Sentinel-1 SAR imagery offers all-weather, day-and-night observation capability, but existing deep learning approaches treat landslide detection as a single-pass segmentation problem, which limits performance in complex terrain where backscatter changes are confounded by soil moisture, surface roughness, urban double bounce, shadow, and layover effects. MAD-SAR, a rule-based agentic framework that coordinates anomaly detection, super-resolution, object detection, and semantic segmentation under a planning orchestrator and a physics-aware validation engine is proposed. The orchestrator selects specialist modules, their execution order, and the number of refinement iterations according to a scene complexity score computed from SAR-derived statistics. The physics-aware validation engine cross-checks every candidate detection against backscatter change thresholds, DEM-derived slope constraints, and radar geometry masks before any detection is committed to the output. MAD-SAR is evaluated on three Japanese disaster datasets: Hiroshima 2018, Kumamoto 2016, and Ibaraki 2019. On the held-out Ibaraki test event, the framework achieves an F1-score of 0.863 and IoU of 0.759, outperforming all baselines and reducing false alarms by 45% relative to standalone SegFormer. Ablation results confirm that each module contributes to the final performance. These results suggest that multi-module orchestration with embedded physical validation can meaningfully improve SAR-based landslide mapping, though broader validation across regions, sensor configurations, and failure mechanisms remains necessary. Full article

Earthquake-induced landslides represent a critical threat to transportation infrastructure in tectonically active mountainous regions, particularly in tropical volcanic settings where weak, highly weathered geomaterials dominate. This study develops an integrated framework that directly links physically based seismic threshold modelling with real-time landslide monitoring and operational early warning. The approach is demonstrated in the Cugenang area of Cianjur Regency, West Java, Indonesia, which was severely impacted by the moment magnitude (Mw) 5.6 earthquake in 2022. Slopes composed of highly weathered pyroclastic deposits [Plasticity Index (PI) = 54–68%; porosity > 60%] exhibit low shear strength and high sensitivity to seismic loading. Limit equilibrium analysis using the Morgenstern–Price method that combines the influence of seismic loading and groundwater conditions suggests that a horizontal seismic coefficient (kh) of approximately 0.06, corresponding to a Peak Ground Acceleration (PGA) of about 0.12 gravitational acceleration (g), is a critical threshold for initial landsliding. This comparatively low threshold challenges commonly reported values and demonstrates that slope failure in tropical volcanic terrains can occur under moderate ground shaking, reinforcing the need for site-specific hazard characterisation. The derived thresholds are operationalised within a multi-sensor early warning system integrating Micro-Electro-Mechanical Systems (MEMS) accelerometers and inclinometer measurements. Three hazard levels—Normal (<0.06 g), Alert (0.06–0.12 g), and Emergency (≥0.12 g)are combined with deformation thresholds [<10 milimeter (mm), 10–30 mm, >30 mm] to capture progressive failure processes and minimise false alarms. By coupling geotechnical modelling and real-time monitoring, this study provides a transferable and scalable framework for enhancing infrastructure resilience in landslide-prone regions. Full article

Landslides are the most common geological hazards, and chemical reinforcement is an effective method for enhancing the stability of soil slopes. Based on the coupled Eulerian–Lagrangian method, finite element analyses were conducted to develop a simple design approach for soil slope reinforcement using the curing agent. First, the effects of internal friction angle, cohesion, soil unit weight, slope height and angle on the slope stability were systematically quantified through 93 numerical cases. On this basis, an empirical formula was established for the factor of safety (FOS) of soil slope, and a method for determining the failure mode was proposed using a dimensionless parameter and two critical values related to slope angle. Subsequently, the reinforcement performance of the SH curing agent was investigated by varying the reinforcement position and length. The results indicate that the reinforcement of Case I-II-III and Case I-II provide the best performance, and the optimum reinforcement length was determined for different slope conditions. For slope angles ranging from 25° to 65°, the FOS after reinforcement was found to increase by 12.1% to 18.8% compared with that before reinforcement. Based on the FE results, empirical formulae for predicting the FOS of reinforced slope were further developed. Finally, a simple design approach was proposed for soil slope reinforcement with curing agent. The proposed method provides a convenient and effective reference for engineering practice in soil slope reinforcement with curing agents. Full article

Accurate prediction of slope displacement from high-frequency GNSS monitoring data is critical for early warning of landslides and tailings dam failures. However, existing deep learning approaches often neglect the spatial coordination imposed by geological structures and fail to decouple abrupt deformation signals from background noise, leading to non-physical oscillations and inconsistent long-term predictions. To address these limitations, this paper proposes a Symmetry-Aware Physics-Guided Spatio-Temporal Graph Network (PG-STGN). First, a geological hierarchy-aware graph is constructed by integrating geometric proximity with prior knowledge of exploration levels, where the resulting adjacency matrix is symmetric by design and reflects the physical symmetry of deformation interactions among monitoring points at the same elevation. A hierarchical masking mechanism restricts feature aggregation to physically connected neighborhoods while preserving this symmetry. Second, an improved dual-path temporal convolutional network (iTCN) decouples high-frequency abrupt variations from low-frequency evolutionary trends, enabling both sensitive detection of sudden deformation and stable tracking of long-term creep. Third, a physics-consistent loss function combining first-order temporal differencing and graph Laplacian regularization enforces kinematic smoothness and spatial coordination; the Laplacian itself is derived from the symmetric adjacency matrix, ensuring symmetric regularization across the monitoring network. Evaluated on a real-world slope GNSS dataset from a large-scale mining project, PG-STGN reduces mean squared error (MSE) by approximately 23.7% and achieves a global R² of 0.924, outperforming state-of-the-art spatio-temporal models. Ablation studies confirm that the symmetric physics-guided graph, dual-path decoupling, and consistency loss are each essential for suppressing spurious correlations and maintaining physically plausible predictions. The proposed framework provides a robust, interpretable, and symmetry-constrained solution for automated slope monitoring under complex geological conditions. Full article

Both variability in precipitation and rainfall extremes are key drivers of landslide activity, yet their combined influence with antecedent moisture conditions remains insufficiently quantified at regional or local scales. In this study, daily precipitation records over the past 25 years (2000–2024) were analyzed for five meteorological stations in Northern Croatia across multiple temporal scales. The aim was to investigate the impact of precipitation patterns and regime changes on landslide triggering in Hum na Sutli and the wider area. Statistical analyses (linear regression, Mann–Kendall trend assessment, and Pearson correlation) were applied, and antecedent wetness was quantified using the antecedent precipitation index (API). Results indicate weak, statistically insignificant positive trends in annual precipitation, accompanied by strong interannual variability and coherent regional behavior. Seasonal analysis reveals the dominance of warm-season precipitation with pronounced extremes, while short-duration and multi-day rainfall events exhibit high variability and clustering. The 2024 Hum na Sutli landslide coincided with elevated cumulative precipitation and sustained high API values, despite the absence of exceptionally extreme single-day rainfall events. These findings highlight the critical role of antecedent moisture accumulation combined with episodic high precipitation in slope failure. The study supports a conceptual model in which landslide triggering is governed by the interaction of preconditioning and short-term hydrometeorological factors, providing a basis for improved hazard and risk assessment. Additionally, preliminary rainfall threshold values are proposed as practical early-warning guidance for local communities in landslide-prone regions in Northern Croatia. Full article

Slope stability description through mechanical laws has important implications for Earth morphology understanding and risk assessment. Previous research studies have shown that shear, tensile, and hybrid fractures can be observed experimentally and in the field, but their description by a single equation is still an open debate. Fracture envelopes able to contemporaneously describe these three fracture modes differ significantly from the Mohr–Coulomb law. Despite the need to apply such a law at all scales, from the laboratory to the mountain range, the fracture criterion that characterizes all types of fractures is rarely used in geotechnical engineering and geological investigations. In order to analyze the stability thresholds of large-scale relief, the current work examines the effects of considering the Griffith criterion with variable rock traction instead of the Mohr–Coulomb law using a modeling approach. The difference estimated for the maximum relief using these two different rupture criteria could be of the same order as those caused by geological phenomena, such as with or without seismic activity, or those caused by destabilization processes (tilting vs. landslide). When compared to the modified Griffith criterion, the Mohr–Coulomb law tends to overestimate the maximum escarpment height. The results are examined in relation to Carrara marble, which serves as a case study for the theoretical framework. Full article

Concentrated rear runoff is an important hydraulic factor that promotes slope instability and flow-like transport characteristics in mountainous landslides; however, the deformation–failure process of slopes and their response relationships under different runoff intensities remain unclear. In this study, the Shaziba landslide in Enshi, Hubei Province, China, was selected as the research object. Two-dimensional flume model tests were conducted under four runoff discharge conditions of 7, 15, 27, and 35 mL/s to investigate the effects of runoff intensity on the hydraulic response and failure mode of the slope. The results show that, as the runoff discharge increased from 7 to 35 mL/s, the initial response times of water content, pore water pressure, and earth pressure at the rear edge decreased from 1205, 1488, and 888 s to 160, 248, and 112 s, respectively. Meanwhile, the gully formation time shortened from 6810 to 336 s, and the time of the first evident collapse decreased from 5758 to 650 s. Under low-runoff conditions, slope deformation was dominated by infiltration-induced softening and progressive creep. Under moderate to high runoff conditions, gully incision and gully-wall collapse accelerated slope disintegration, resulting in soil–water mixed transport and enhanced mobility of failed materials. Concentrated rear runoff drives the slope through successive stages of initial deformation, structural disintegration of the slope, flow-like failure, and toe deposition. These findings provide experimental evidence for the identification and prevention of landslides controlled by rear runoff. Full article

Landslide detection has largely relied on supervised learning or DEM-based representations, which can limit rapid deployment and generalization across heterogeneous terrain. In this work, we present a zero-shot, fully unsupervised framework that identifies landslide-like geomorphic instability candidates from raw UAV-mounted LiDAR, removing the need for labeled data, pre-event baselines, or rasterized terrain abstractions. Our approach is motivated by the observation that landslides manifest as localized geometric inconsistencies in the terrain surface. We capture this through a multi-scale formulation that combines point-level and cluster-level indicators of instability. At the point level, a PCA-based residual depth metric reduces slope-induced bias and highlights surface discontinuities, while local concavity captures terrain depletion patterns. At the cluster level, geomorphometric descriptors such as curvature concentration, surface roughness, elevation discontinuity, and slope variation are extracted using density-aware 3D clustering and integrated through adaptive feature fusion. The resulting probabilistic instability field enables spatially coherent delineation of landslide scars, including rupture boundaries, displaced material, and emerging failure regions. In addition, the detected patches provide useful priors for post-event susceptibility analysis without requiring temporal observations. Experiments across diverse geomorphic settings show that the proposed method improves detection of subtle terrain disturbances compared to DEM-based pipelines and supervised learning approaches, while remaining robust to noise and terrain variability. Overall, this work demonstrates that geometry-driven, unsupervised inference on raw 3D data can serve as a practical and scalable alternative for near real-time landslide detection using UAV-based systems. Full article

Rainfall contributes to slope instability when infiltrating water reduces matric suction and elevates pore water pressure beyond critical thresholds. Empirical rainfall intensity–duration (I-D) thresholds define the minimum rainfall conditions necessary to initiate landslides and are widely adopted in regional early warning systems. This study derives I-D thresholds for shallow landslide initiation in South-East Queensland (SEQ), Australia, using quantile regression applied to 104 rainfall-induced shallow landslide events recorded between 1974 and 2018. Thresholds at the 2nd, 10th, 50th, and 90th percentiles were derived over a duration range of 0.3 to 383 h and intensity range of 0.15 to 13.7 mm h⁻¹. The 2nd percentile, adopted as the conservative regional early warning threshold, is expressed as I = 0.719 × D^−0.220, where I is rainfall intensity (mm h⁻¹) and D is event duration (h). To facilitate inter-regional comparability, normalised thresholds expressed in terms of mean annual precipitation (MAP) were also derived, yielding a 2nd percentile threshold of I_MAP = 6.070 × 10⁻⁴ × D^−0.207. Both I-D and I_MAP -D thresholds fall substantially below existing global benchmarks, reflecting the pronounced susceptibility of SEQ’s deeply weathered residual soils to infiltration-driven failure. Independent validation against real-time tilt sensor and volumetric water content monitoring data from five kinematic failure events recorded at Maleny, Queensland (2016–2020), confirmed that all events plotted above the 2nd percentile threshold, with zero false negatives. The results provide a quantitative, operationally validated framework for regional shallow landslide early warning in subtropical Australia. Full article

The behavior of buried pipes constructed on slopes is of great importance for the safety and sustainability of infrastructure systems. Slope movements, landslides, and soil displacements can significantly affect pipe–soil interaction, creating additional stresses on the pipes. Therefore, accurately determining the behavior of pipes under slope conditions is essential for improving engineering designs and preventing potential damage. In recent years, advanced experimental methods have been widely used in soil mechanics and geotechnical engineering studies to determine deformation and displacement fields. In this study, the behavior of buried pipes in reinforced and unreinforced sloping soils was experimentally investigated. Particle Image Velocimetry (PIV), an advanced image-based technique, was used to analyze soil deformation and displacement fields based on the images obtained during the model experiments. The results indicate that geogrid reinforcement has a significant effect on the behavior of the buried pipe and the deformation patterns of the soil. The study is primarily intended as a mechanism-oriented experimental investigation rather than an extensive parametric optimization study. Through PIV-based full-field displacement analyses, the evolution of deformation zones and failure surfaces around the buried pipe was evaluated in detail. Full article

A large landslide struck the Zhenba Dahekou area of Shaanxi Province, China, on 9 September 2021. To accurately extract landslide displacement in emergency situations, in this study, we explore the feasibility and effectiveness of using satellite images and post-failure emergency UAV images to investigate the large displacement of the landslide through the pixel offset tracking (POT) method and evaluate four different POT methods, including NCC operated in the spatial domain (NCC), the orientation correlation method in ImGRAFT software (ImGRAFT), the multi-pass method in GIV software (GIV), and the frequency method in COSI-Corr software (COSI-Corr). It is found that the Zhenba landslide has moved southwest by about 74.3 m~96.4 m with the sliding direction between 231°~258°. The southward displacement of the landslide gradually decreases from southeast to northwest, and the westward displacement on the west side is greater than that on the east side. The relative matching precision of the POT methods in stable areas reached 0.8 m, superimposed on an image registration RMSE of 1.2 m. Under the experimental conditions of this study, ImGRAFT demonstrated robust overall performance. In terms of matching ability, ImGRAFT and NCC outperform GIV and COSI-Corr. In terms of displacement gradients expression ability, ImGRAFT and COSI-Corr outperform NCC and GIV; in terms of matching efficiency, COSI-Corr, GIV, and ImGRAFT are far superior to NCC. This study expands the application of multi-source optical data to investigate landslides and provides suggestions for the displacement extraction of large-displacement landslides, which will be helpful for the emergency investigation and research of landslides in the future. Full article

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

Earthquake-induced landslides are a major secondary seismic hazard in mountainous regions and can cause significant human and economic losses. This study presents COSISA (Co-Seismic Slope Instabilities Susceptibility Assessment), a software tool developed in Python and GIS to automate the generation of co-seismic landslide susceptibility maps based on the Newmark displacement method combined with a logic-tree approach. The software integrates geomorphological, geotechnical, and seismic data to compute Newmark displacement using several available empirical equations. The logic-tree framework incorporates the variability and uncertainty of geotechnical parameters, failure depth, degree of saturation, and empirical models through weighted combinations of input variables. As a result, COSISA produces numerous susceptibility maps corresponding to different parameter combinations and generates a weighted susceptibility map. The tool was applied to a case study in the Granada Basin (southeastern Spain), an area affected by the 2021 Santa Fe seismic sequence. Results show that COSISA efficiently generates multiple susceptibility scenarios and identifies best- and worst-case conditions, significantly reducing the time and effort required compared with conventional step-by-step procedures. This approach supports seismic hazard assessment and can contribute to territorial planning and risk management strategies aimed at reducing damage from future co-seismic landslides. Full article

Given the escalating impacts of global climate change and extreme weather events, the accurate stability assessment of rainfall-induced landslides necessitates a comprehensive consideration of both seepage processes and the inherent spatial variability of soils. Traditional deterministic and two-dimensional (2D) analyses often fail to capture the multi-dimensional kinematic features of slope failures and the stochastic nature of soil heterogeneity, thereby leading to inaccurate risk assessments. This study proposes a three-dimensional (3D) slope reliability analysis framework. Within this framework, a 3D slope geometric model is constructed using GeoStudio 2025.1.0 software, and seepage analysis is conducted by the SEEP3D module. To account for soil spatial variability, the Karhunen–Loève (K-L) expansion method is employed to discretize key shear strength parameters (effective cohesion and effective angle of internal friction). The factor of safety (F_s) is evaluated using the 3D simplified Bishop method, which is then coupled with Monte Carlo simulations to determine the probability of failure (P_f). The results show that rainfall infiltration causes progressive dissipation of shallow matric suction and a significant rise in the groundwater table near the slope toe, resulting in reduced effective stress in the critical resistance zone. As rainfall intensity increases, the F_s decreases approximately linearly from 1.14 to 0.90, whereas the P_f increases nonlinearly from nearly 0 to 98.36%. Under the rainstorm condition, although the F_s remains above unity at 1.063, the corresponding P_f reaches 23%, indicating that deterministic evaluation based only on the F_s may underestimate the actual failure risk. The proposed framework provides a quantitative tool for evaluating rainfall-induced slope instability by integrating transient hydraulic response, three-dimensional spatial variability, and probabilistic reliability assessment. Full article

Cascading failures of clustered landslide dams can intensify downstream hazards not only by increasing peak flood magnitude but also by accelerating the rise of water level immediately upstream of successive dams, thereby shortening the available response time before overtopping. This study reports large-scale flume experiments on a three-dam cascade built with identical geometry and similar soil gradation, while systematically varying longitudinal spacing and inflow discharge. The principal measured variable, $C_w\left(t\right)$, is defined here as the local forebay run-up/water-level record measured at a fixed gauge position immediately upstream of each dam. The run-up hydrographs were summarized using peak run-up $C_w^{\mathrm{m}\mathrm{a}\mathrm{x}}$, threshold-arrival time $t_a$ defined at $0.1 C_w^{\mathrm{m}\mathrm{a}\mathrm{x}}$, time to peak $t_p$, maximum rising-stage rate $S^{\mathrm{m}\mathrm{a}\mathrm{x}}$, and above-threshold duration $T$. Across ten tests (five spacing configurations under low/high discharge), peak run-up at both downstream dams consistently exceeded that at Dam1, with amplification factors relative to Dam1 of 1.11–1.45 at Dam2 and 1.13–1.42 at Dam3; Dam3 was not always higher than Dam2. Amplification was much stronger in the rising-stage dynamics: $S^{\mathrm{m}\mathrm{a}\mathrm{x}}$ increased relative to Dam1 by factors of 1.56–11.0 at Dam2 and 2.27–14.0 at Dam3, demonstrating pronounced downstream wavefront steepening. Higher discharge produced earlier threshold arrivals and peaks throughout the cascade, whereas shorter spacing generally produced more impulsive downstream responses with sharper peaks and larger rate amplification. Overall, the dataset provides stage/run-up-based constraints on cascade amplification and indicates that, within the present experimental matrix, dam spacing is the dominant geometric control on flood propagation and downstream hazard escalation. Full article