Search Results (353)

Failures at large-scale open-pit mines and tailing dams pose critical risks to human life, environmental systems, and economic sustainability. Although Interferometric Synthetic Aperture Radar (InSAR) has proven effective in detecting long-term surface deformation, a scientifically robust early-warning framework has not yet been established because standardized quantitative thresholds that are capable of distinguishing benign consolidation settlement from instability-driven deformation remain unavailable.InSAR has proven effective for detecting long-term surface deformation. However, a scientific early-warning framework has not yet been proposed or developed due to the absence of standardized quantitative thresholds that distinguish benign consolidation settlement from instability-driven deformation. This research proposes a novel InSAR-based Failure-Risk Index (FRI) that integrates displacement, velocity, and, most importantly, deformation acceleration into a single, normalized metric as an early warning system for mining infrastructure instability. The framework that we propose (i) emphasizes acceleration as a leading indicator of change in mechanical regime, (ii) incorporates a statistically guided separation of long-term consolidation settlement from anomalous deformation based on baseline variability, (iii) applies a statistical standardization and change-point detection system. The methodology is validated through a retrospective analysis of the heap leach failure—that occurred in Çöpler Gold Mine in Erzincan, Türkiye, on 13 February 2024—by using a set of Sentinel-1 time-series images collected between 2014 and 2024. The results prove that while displacement and velocity remained within ranges typically interpreted as stable, deformation acceleration exhibited a statistically significant increase that began around 2020, exceeded baseline variability by approximately two orders of magnitude, which is approximately four years before the collapse, and marked the onset of tertiary creep and progressive instability. The proposed FRI framework successfully captures this transition and provides a transferable, meaningful early-warning framework to support proactive risk management and improve the safety of mining infrastructure. Full article

Landslide-generated impulse waves in deeply incised gorge regions pose significant risks to hydropower infrastructure, particularly under fluctuating reservoir water levels. This study investigates the potential global instability sliding of the Sejiang deforming slope on the left bank of the Bala Hydropower Station, Sichuan Province of China. A three-dimensional numerical simulation of the landslide–water entry, wave generation, and propagation processes conducted using computational fluid dynamics (CFD) shows a maximum wave height of 5.57 m on the opposite bank. Moreover, the CFD results were compared with those derived from Pan′s empirical formula method. In contrast, Pan′s empirical formula only provides conservative predictions for water-entry velocity and wave height, with a maximum wave height of 13.60 m on the opposite bank. Notably, the CFD numerical simulations precisely characterize complex topographic effects, such as the approximately 156% wave height amplification at the spoil disposal bend due to reflection and superposition, as well as the localized energy convergence in front of the dam. Furthermore, the impulse wave destructive potential is positively correlated with reservoir water levels. While the assessment confirms no risk of dam overtopping under the current scenarios, it highlights the necessity for differentiated protection strategies targeting three critical zones, i.e., the initial wave impact zone, the multi-directional superposition zone at the spoil disposal area, and the localized energy convergence zone near the dam. This study provides a reliable quantitative basis for refined hazard assessment and disaster mitigation in complex reservoir topographies. Full article

Long-term dam safety assessment relies on continuous monitoring data from multiple spatially distributed measurement points. However, monitoring data are affected by measurement noise, environmental disturbances, and model errors, while the spatial correlation among monitoring points is often ignored, leading to biased uncertainty estimation and unreliable prediction intervals. To address these issues, this study proposes a spatial-correlation-aware distribution-adaptive interval prediction method for dam monitoring via two-level uncertainty fusion. Measurement random noise is first separated using a filtering strategy, and its uncertainty is updated by incorporating the spatial correlation among multiple monitoring points. A regression model is then established based on the filtered monitoring data, and model prediction uncertainty is quantified from the residual distribution. The two uncertainty components are further integrated to construct distribution-adaptive asymmetric prediction intervals. The proposed method is verified using deformation monitoring data from the PB high core rockfill dam. The results show that the proposed method achieves an average PICP of 0.9898 on the training set, close to the target coverage level of 0.99, while reducing the average NMPIW by 16.9% and 13.4% compared with the symmetric interval method and the traditional 3σ method, respectively. On the validation set, the average NMPIW is further reduced by 18.4% and 26.5%, demonstrating that the proposed method can provide more compact and informative prediction intervals for refined dam safety monitoring. Full article

Accurate detection and characterization of surface defects in concrete dams is vital for ensuring safe operation. To address the limitations of existing research focused solely on cracks and the challenges traditional convolutional networks face in adapting to deformation and multiscale features, this study introduces DCN-YOLO, a deformable convolution-augmented framework for the simultaneous detection and classification of multiple defect types from UAV-acquired imagery. The model outputs bounding box localizations and categorical labels. Based on YOLOv12, this proposed model integrates DCNv4 deformable convolutions with the C3k2 module. By leveraging adaptive sampling offsets and dynamic modulation, the proposed model enhances geometric modeling for irregular defects, improving the detection of small and medium defects while achieving an acceptable trade-off in inference efficiency. To address multiple defect coexistence, we adopt Binary Cross-Entropy (BCE) loss to decouple classification and localization, improving training stability in multi-label scenarios. A Multi-defects dataset was created using UAV images, and performance was validated on the CrackSeg public dataset. The proposed model achieved 77.4% ± 0.2% overall precision under complex conditions, exceeding the YOLOv12l baseline by 7.1% and improving mAP_50-95 by 4.2%. It demonstrated competitive performance in detecting cracks, aggregate exposure, and construction joints, thereby providing a potentially robust and efficient approach for intelligent inspection of concrete dam surface defects. Full article

Global Navigation Satellite System Reflectometry Interferometric Synthetic Aperture Radar (GNSS-R InSAR) offers all-weather, all-day observation capabilities and high temporal resolution, enabling elevation deformation monitoring with a single satellite. However, in hazardous regions, such as tailings dam slopes, measuring the deformation of a greater number of target points is essential for a more accurate assessment of geological hazard risks. Since navigation satellite signals are not originally designed for imaging purposes, their inherent narrow bandwidths result in low spatial resolution and limited target recognition capabilities, rendering them inadequate for such scenarios. To address these limitations, this paper investigates a GNSS-R InSAR deformation measurement architecture utilizing dual-frequency BeiDou-3 (BDS-3) signal fusion. Specifically, a coherent spectrum fusion method is introduced to effectively expand the signal bandwidth, thereby significantly enhancing range resolution and target identification capabilities. Building upon this, deformation measurements are conducted to achieve more refined and detailed monitoring. Full article

High-altitude rock slides frequently occur in the high-mountain canyon regions of the eastern Tibetan Plateau, posing significant disaster risks. The Baige landslide catastrophically failed in October 2018, blocking the Jinsha River and forming a major landslide-dammed lake. However, quantitative understanding of the spatiotemporal evolution and environmental control mechanisms remains insufficient, particularly regarding stage-dependent driving mechanisms. This study investigates the Baige landslide using mall Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR), Seasonal-Trend decomposition based on Loess (STL) time-series decomposition, Principal Component Analysis–Independent Component Analysis (PCA-ICA) signal analysis, and slope-unit spatial statistics. Results indicate that: (1) deformation exhibited three stages separated by October 2018: slow pre-slide deformation, post-slide residual creep, and long-term sustained acceleration; (2) instability caused systematic restructuring of the deformation field, with valid pixels decreasing from 2766 to 560, deformation changing from slight positive line-of-sight (LOS) displacement to pronounced negative LOS displacement, and global standard deviation increasing from 21.40 mm to 40.55 mm, with stronger disturbances in the steep front zone; and (3) the driving mechanism shifted from short-term multi-factor control to a temperature-dominated long-term environmental control regime after failure, while gravity-driven creep and post-failure structural adjustment remained important background controls. Slope fragmentation and structural reorganization likely contributed to this transition. Full article

Monitoring dam deformation is critical for ensuring structural integrity and identifying long-term settlement trends. However, traditional InSAR techniques often face limitations in tropical environments due to severe temporal decorrelation. This study addresses these challenges at Jatiluhur Dam, Indonesia, by implementing an integrated framework using Sentinel-1 InSAR, in situ leveling, GNSS, and reservoir water-level data from 2019 to 2024. To overcome the observation bottlenecks, Tracy–Widom-guided PSI (TW-PSI) was employed and compared against SBAS and conventional PSI. The TW-PSI approach successfully increased on-structure measurement point density by approximately 40%, supporting a first-order ascending–descending decomposition into east–west and quasi-vertical components. The analysis reveals a persistent settlement bowl at the central crest (C7–C12), consistent with long-term leveling observations and supported by regional GNSS trend checking. While the 2022 Mw 5.6 Cianjur earthquake showed no statistically significant co-seismic crest deformation, a strong correlation (r = −0.709) was identified between crest deformation and reservoir water-level variations, suggesting an observational association between reservoir level and crest settlement tendency. Furthermore, the application of the Annual Structural Deformation Tolerance Ratio (ASDTR) identified specific priority monitoring zones. These findings demonstrate that the proposed integrated framework can support operational dam deformation monitoring by linking satellite-derived measurements with in situ observations and engineering-oriented interpretation. Full article

Tailings storage facility (TSF) failures have caused severe casualties and economic losses. This study used Enhanced Small Baseline Subset InSAR (E-SBAS-InSAR) and 88 Sentinel-1A images to retrieve the 2022–2024 surface deformation time series of the Shiguilong TSF, located in the Fe–Cu polymetallic metallogenic belt of the middle–lower Yangtze River. The reliability of the results was assessed through consistency comparisons with Small Baseline Subset InSAR (SBAS-InSAR) and Persistent Scatterer InSAR (PS-InSAR). A time-series decomposition model was applied to extract seasonal deformation components and analyze their lagged responses to temperature and intense rainfall events. The results show that: (1) E-SBAS-InSAR achieved a monitoring-point density nearly 7 times higher than SBAS-InSAR, enabling dense and long-term deformation characterization; (2) subsidence at Shiguilong continued to increase, with cumulative subsidence reaching −76.8 mm and a maximum annual mean subsidence rate of −22.78 mm/yr; (3) deformation was mainly controlled by long-term consolidation of loose tailings and creep of dam–tailings materials, while seasonal factors induced stage-dependent fluctuations; (4) seasonal deformation showed lagged responses of 6 days to temperature variations and 2 days to intense rainfall events, with rainfall exerting a more pronounced influence. This work is significant for TSFs monitoring under complex surface conditions. Full article

Using an upstream tailings pond in Beijing as the prototype, this study constructed a 1:150 physical model to investigate the internal response and overtopping failure evolution of tailings dams under continuous rainfall (T1) and intermittent rainfall (T2). The results showed that the cumulative rainfall duration at failure in T2 was approximately 11.1% shorter than that in T1. The duration from local overtopping to overall failure in T2 was 40% shorter than that in T1. Under T2, during the first rainfall interval, the average rise rates of the phreatic line, volumetric water content, pore water pressure, and earth pressure were approximately 0.35, 0.42, 0.29, and 0.32 times the corresponding rise rates under T1, respectively. During the second rainfall interval, the average decline rates of the phreatic line, pore water pressure, and earth pressure were approximately 1.83, 2.79, and 3.52 times the corresponding rise rates under T1, respectively. Overtopping failure under both rainfall patterns was a composite instability process controlled by internal seepage weakening and overtopping erosion. The process can be divided into four stages: toe seepage, dam deformation, local overtopping, and overall sliding failure. These findings indicate that rainfall pattern and internal response should be considered in tailings dam risk identification. 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

Reservoir water level fluctuations and rainfall drive bank slope deformation, typically exhibiting spatiotemporal lags. Existing prediction models often fail to characterize these complex coupled relationships and rely on manual variable selection, causing information loss and reduced performance. To address these issues, a novel multi-point prediction model for reservoir bank slope deformation based on lag-aware clustering (LAC), referred to as LAC-MOGP, is proposed in this study. First, the Maximal Information Coefficient (MIC) quantifies the lag between influencing factors and deformation for objective factors screening. Next, an improved dynamic time warping (DTW) algorithm with a lag-difference-constrained matching window is combined with affinity propagation (AP) to cluster monitoring points based on asynchronous temporal correlations. Finally, a DTW-based similarity weighting scheme is embedded into a multi-output Gaussian Process (MOGP) kernel to refine covariance modeling and predict deformation within each cluster. Validated using observations from the Jinlongshan slope of the Ertan arch dam, the proposed model outperformed traditional methods in prediction accuracy and long-term stability. Achieving the lowest average root mean square error (2.677 mm) and average mean absolute error (2.325 mm), the LAC-MOGP model demonstrates significant effectiveness and practical applicability for reservoir slope deformation forecasting. Full article

In central Mexico, ground failure and subsidence have accelerated, as evidenced by the Villa de Reyes graben, particularly at the El Hundido Dam, with the primary cause attributed to groundwater overexploitation. This study integrates electromagnetic profiling (EMP), electrical resistivity tomography (ERT), and transient electromagnetic (TEM) surveys to determine the origin of the fractures at the El Hundido Dam. Based on the TEM survey, a geoelectric section was obtained that models the depth and morphology of the igneous bedrock. At the El Hundido Dam, the igneous basement exhibits convex deformation due to transpressional stresses, which favours the formation of a positive flower-type fault structure. Deformations caused by the basement topography and the fault system are evident in the 100 m-thick Quaternary sequence, as revealed by ERT studies. ERT and EMP surveys showed the presence of a clayey layer that acted as a barrier to surface water infiltration, allowing it to be stored in the past, and which is now destroyed by fractures. Although the drop in the water table has contributed to polygonal cracking, hydro-compaction, and ground subsidence, local tectonics is the primary factor controlling subsoil faulting at the El Hundido Dam. Full article

Fluid–structure interaction (FSI) research is crucial for applications in fields such as naval engineering, geological hazards, and biomechanics. Traditional grid-based methods (such as CFD) often face challenges in simulating large-deformation flow fields and complex boundary conditions, where mesh distortion can compromise simulation accuracy. Building upon the DualSPHysics5.2 framework, this study leverages the strengths of weakly compressible SPH (WCSPH) in modeling free surface flows and large-deformation fluids, as well as the discrete element method (DEM), for accurately describing particle collisions and fragmentation behaviors. We propose an improved MSPH-DEM coupling algorithm that incorporates moving least squares (MLS) correction for kernel function gradient optimization. This algorithm utilizes MLS-based gradient correction to achieve smoother fluid surfaces as well as bidirectional coupling between fluids and particles. Experimental validation demonstrates that in dam break simulations, this method reduces pressure errors. In the dam break impacting a cube experiment, it enhances accuracy, while in the dam break impacting a baffle experiment, the horizontal displacement of marker points closely aligns with the experimental values from Liao et al. This approach effectively improves the accuracy of the simulations of FSI problems, offering a more reliable numerical simulation methodology for engineering applications such as geological hazard prevention. Full article

The mechanical performance of concrete cutoff walls in deep overburden is decisive for dam safety. Current coarse mesh models struggle to accurately simulate their response under complex conditions. In this paper, a refined numerical model is established specifically for a concrete cutoff wall in deep overburden. The deformation and stress characteristics and mesh sensitivity of the cutoff wall are systematically investigated. A quantitative index of overstress area ratio is introduced innovatively, and the effects of cutoff wall mesh size along the thickness direction, dam height, and overburden parameters on the deformation and stress characteristics of the cutoff wall are explored in detail. The results show that the stress characteristics of the cutoff wall requires a fine mesh model with an element thickness ≤ 1/4 of the cutoff wall. The change in dam height and overburden parameters mainly affects the stress magnitude of the cutoff wall but does not change its tensile stress distribution pattern. The variable-size mesh generation achieves collaborative optimization of accuracy and efficiency, and the calculation amount is significantly reduced by about 16%, with error below 5%. This study presents an efficient method and can provide technical support for the safety evaluation of concrete cutoff walls in deep overburden. Full article

The stress and deformation sensitivity analysis of high earth-rock dams requires knowledge of the statistical mean and standard deviation of deformation parameters of dam materials. However, these parameters are typically determined through grouped tests and sorting. Given the small sample size in each group and the consequently large parameter errors, the inaccuracy of the resulting statistical parameters is evident. The least squares method fits all test points of each group in the same coordinate system for regression calculation, which not only helps to better address the issue of a small sample size, but also eliminates the errors caused by the grouping of test parameters. However, it is found that when the least squares method is applied to the elastic modulus and bulk modulus parameters of the Duncan–Chang E-B model, the residual errors have heteroscedasticity and correlation, which violates the use condition of the least squares method. In order to eliminate the heteroscedasticity and correlation of the fitting residuals of the elastic modulus and bulk modulus parameters of the Duncan–Chang E-B model, this paper decomposes the covariance matrix of the regression residuals to obtain its square root matrix, multiplies the explanatory variables, dependent variables and residual vectors of the regression equation by the square root matrix of the covariance, respectively, and performs variable substitution. The new regression equation has the homogeneity of variance and the irrelevance of the residual. The mean and variance of the model parameters are obtained directly by calculating all the experimental data. The variance of the new parameters is smaller than that of the classical least squares method. The results demonstrate that this generalized least squares method improves the estimation accuracy of elastic modulus and bulk modulus parameters of the Duncan–Chang E-B model. Full article