Search Results (263)

In weak carbonate rock masses, small-sized karst features ranging from greater than 2 cm to over 1 m in diameter can significantly compromise slope stability, yet they are often overlooked in traditional geotechnical models. This study employs the equivalent porous medium (EPM) approach to incorporate these small-sized voids into two-dimensional finite element slope stability analysis using RS2 software (Version 11.022). By treating the matrix of karst hollows as a porous continuum, we simulate the mechanical and hydraulic influence of their presence on pit slope performance. Results show that even small voids substantially reduce the factor of safety, with destabilization intensifying as void density and pore fluid infiltration increase. Distinct failure mechanisms—including circular sliding, localized subsidence due to cavity collapse, and rockfalls from intersecting shear planes—emerge from the simulations. The stress trajectories and yield elements highlight how minor voids influence the distribution and initiation of shear and tensile failures. These findings reveal that karst features previously considered negligible can be critical structural discontinuities that trigger failure. The EPM framework thus provides a computationally efficient and mechanistically sound means of modelling the cumulative impact of small-sized karst features, bridging a significant gap in geotechnical design for karst-prone weak rock slopes. Full article

Aiming at the lightweight design of a bridge-shed integration structure, this paper presents a three-layered absorbing system in which a part of the sand cushion is replaced by expanded polystyrene (EPS) geofoam and the reinforced concrete (RC) protective slab is arranged above the sand cushion to enhance the composite system’s safety. A three-dimensional Smoothed Particle Hydrodynamics–Finite Element Method (SPH-FEM) coupled numerical model is developed in LS-DYNA (Livermore Software Technology Corporation, Livermore, CA, USA, version R13.1.1), with its validity rigorously verified. The dynamic response of rockfall impacts on the shed slab with composite cushions of various thicknesses is analyzed by varying the thickness of sand and EPS materials. To optimize the cushion design, a specific energy dissipation ratio (SEDR), defined as the energy dissipation rate per unit mass (η/M), is introduced as a key performance metric. Furthermore, the complicated interactional mechanism between the rockfall and the optimum-thickness composite system is rationally interpreted, and the energy dissipation mechanism of the composite cushion is revealed. Using logistic regression, the ultimate stress state of the reactive powder concrete (RPC) slab is methodically analyzed, accounting for the speed and mass of the rockfall. The results are indicative of the fact that the composite cushion not only has less dead weight but also exhibits superior impact resistance compared to the 90 cm sand cushions; the impact resistance performance index SEDR of the three-layered absorbing system reaches 2.5, showing a remarkable 55% enhancement compared to the sand cushion (SEDR = 1.61). Additionally, both the sand cushion and the RC protective slab effectively dissipate most of the impact energy, while the EPS material experiences relatively little internal energy build-up in comparison. This feature overcomes the traditional vulnerability of EPS subjected to impact loads. One of the highlights of the present investigation is the development of an identification model specifically designed to accurately assess the stress state of RPC slabs under various rockfall impact conditions. Full article

Most public seismic recordings, sampled at hundreds of Hz, tend to be unlabelled, i.e., not catalogued, mainly because of the sheer volume of samples and the amount of time needed by experts to confidently label detected events. This is especially challenging for very low signal-to-noise ratio microseismic events that characterise landslides during rock and soil mass displacement. Whilst numerous supervised machine learning models have been proposed to classify landslide events, they rely on a large amount of labelled datasets. Therefore, there is an urgent need to develop tools to effectively automate the data-labelling process from a small set of labelled samples. In this paper, we propose a semi-supervised method for labelling of signals recorded by seismometers that can reduce the time and expertise needed to create fully annotated datasets. The proposed Siamese network approach learns best class-exemplar anchors, leveraging learned similarity between these anchor embeddings and unlabelled signals. Classification is performed via soft-labelling and thresholding instead of hard class boundaries. Furthermore, network output explainability is used to explain misclassifications and we demonstrate the effect of anchors on performance, via ablation studies. The proposed approach classifies four landslide classes, namely earthquakes, micro-quakes, rockfall and anthropogenic noise, demonstrating good agreement with manually detected events while requiring few training data to be effective, hence reducing the time needed for labelling and updating models. Full article

Rock bolts are crucial components in the subterranean support systems in underground mines that provide adequate structural reinforcement to the rock mass to prevent unforeseen hazards like rockfalls. This makes frequent assessments of such bolts critical for maintaining rock mass stability and minimising risks in underground mining operations. Where manual surveying of rock bolts is challenging due to the low-light conditions in the underground mines and the time-intensive nature of the process, automated detection of rock bolts serves as a plausible solution. To that end, this study focuses on the automatic identification of rock bolts within medium- to large-scale 3D point clouds obtained from underground mines using mobile laser scanners. Existing techniques for automated rock bolt identification primarily rely on feature engineering and traditional machine learning approaches. However, such techniques lack robustness as these point clouds present several challenges due to data noise, varying environments, and complex surrounding structures. Moreover, the target rock bolts are extremely small objects within large-scale point clouds and are often partially obscured due to the application of reinforcement shotcrete. Addressing these challenges, this paper proposes an approach termed DeepBolt, which employs a novel two-stage deep learning architecture specifically designed for handling severe class imbalance for the automatic and efficient identification of rock bolts in complex 3D point clouds. The proposed method surpasses state-of-the-art semantic segmentation models by up to 42.5% in Intersection over Union (IoU) for rock bolt points. Additionally, it outperforms existing rock bolt identification techniques, achieving a 96.41% precision and 96.96% recall in classifying rock bolts, demonstrating its robustness and effectiveness in complex underground environments. Full article

Rockfalls, among the most common natural disasters, pose risks such as traffic congestion, casualties, and substantial property damage. Guizhou Province, with China’s fourth-longest highway network, features mountainous terrain prone to frequent rockfall incidents annually. Consequently, assessing highway rockfall risks in Guizhou Province is crucial for safeguarding the lives and travel of residents. This study evaluates highway rockfall risk through three key components: susceptibility, hazard, and vulnerability. Susceptibility was assessed using information content and logistic regression methods, considering factors such as elevation, slope, normalized difference vegetation index (NDVI), aspect, distance from fault, relief amplitude, lithology, and rock weathering index (RWI). Hazard assessment utilized a fuzzy analytic hierarchy process (AHP), focusing on average annual rainfall and daily maximum rainfall. Socioeconomic factors, including GDP, population density, and land use type, were incorporated to gauge vulnerability. Integration of these assessments via a risk matrix yielded comprehensive highway rockfall risk profiles. Results indicate a predominantly high risk across Guizhou Province, with high-risk zones covering 41.19% of the area. Spatially, the western regions exhibit higher risk levels compared to eastern areas. Notably, the Bijie region features over 70% of its highway mileage categorized as high risk or above. Logistic regression identified distance from fault lines as the most negatively correlated factor affecting highway rockfall susceptibility, whereas elevation gradient demonstrated a minimal influence. This research provides valuable insights for decision-makers in formulating highway rockfall prevention and control strategies. Full article

RC piers in mountainous coastal or saline areas face the dual threats of rockfall impacts and chloride-induced steel corrosion, but their combined effects on dynamic response and residual bearing capacity remain unquantified. This study aims to investigate these combined effects over a 90-year service time and propose a damage assessment formula. A validated numerical model (relative error ≤14.7%) of corroded RC columns under impact is developed using ABAQUS, based on which the dynamic response and residual bearing capacity of an actual RC pier subjected to rockfall impacts during the service time of 90 years incorporating corrosion initiation (via Life-365 software 2.2) and propagation are analyzed, with the consideration of various impact energies (1–5 t mass, 5–15 m/s velocity). Results show that (1) increasing impact mass/velocity expands damage and increases displacement (e.g., the velocity of increases peak displacement by 33.41 mm in comparison to 5 m/s); (2) a 90-year service time leads to >50% severe surface damage and 47.1% residual capacity loss; and (3) the proposed and validated damage formula assessment formula for the residual bearing capacity enables lifecycle maintenance guidance. This work provides a validated framework for assessing combined corrosion-rockfall effects, aiding design and maintenance of structures. Full article

This study presents a digital twin–based framework for assessing rockfall hazards at the immediate vicinity of the Rumkale Archaeological Site, a geologically sensitive and culturally significant location in southeastern Türkiye. Historically associated with early Christianity and strategically located along the Euphrates, Rumkale is a protected heritage site that attracts increasing numbers of visitors. Here, high-resolution photogrammetric models were generated using imagery acquired from a remotely piloted aircraft system and post-processed with ground control points to produce a spatially accurate 3D digital twin. Field-based geomechanical measurements including discontinuity orientations, joint classifications, and strength parameters were integrated with digital analyses to identify and evaluate hazardous rock blocks. Kinematic assessments conducted in the study revealed susceptibility to planar, wedge, and toppling failures. The results showed the role of lithological structure, active tectonics, and environmental factors in driving slope instability. The proposed methodology demonstrates effective use of digital twin technologies in conjunction with traditional geotechnical techniques, offering a replicable and non-invasive approach for site-scale hazard evaluation and conservation planning in heritage contexts. This work contributes to the advancement of interdisciplinary methods for geohazard-informed management of cultural landscapes. Full article

In rockfall hazard mitigation, geofoam has been used in the cushion layer to improve the sustainability of the rockfall gallery, such as impact resistance enhancement and dead load reduction. Impact tests were conducted to study the effect of geofoam type, thickness, and impact energy on the impact responses of the sand cushion layer. The test results showed that placing geofoam in the sand cushion can reduce the peak impact force of the rockfall and the peak acceleration of the gallery slab by up to 80%. While the peak impact stress at the cushion layer bottom can also be reduced by geofoam under low impact energy, thicker geofoam layers (e.g., 4 and 6 cm) increased peak impact stress when the rockfall had high impact energy. Placing geofoam at the bottom of the cushion to replace one third of the sand cushion thickness can enhance the impact resistance of the cushion layer. Under low impact energy, expandable polyethylene (EPE) foam resulted in lower impact force on the rockfall, reduced impact stress within the sand cushion, and diminished vibration of the gallery slab compared with polystyrene (EPS) foam with a constant thickness. However, EPS foam is suitable for use in sand cushions of rockfall galleries subjected to high-energy rock impacts. Moreover, EPE foam exhibits superior resilience, resulting in less damage compared to EPS foam. Full article

The analysis of rockfall events provides critical insights for deciphering planetary geological processes and reconstructing environmental evolutionary timelines. Conventional visual interpretation methods that rely on orbiter imagery can be inefficient due to their massive datasets and subtle morphological signatures. While deep learning technologies, particularly object detection models, demonstrate transformative potential, they require specific adaptation to planetary imaging constraints, including low contrast, grayscale inputs, and small-target detection. Our coordinated optimization strategy integrates dynamic cropping optimization with architectural innovations: Kolmogorov–Arnold Network based C3 module (KANC3) replaces RepC3 through Legendre polynomial decomposition to strengthen feature representation, while our dynamic cropping strategy significantly improves small-target detection in low-contrast grayscale imagery by mitigating background and target imbalance. Experimental validation on the optimized RMaM-2020 dataset demonstrates that Real-Time Detection Transformer with a ResNet-18 backbone and Kolmogorov–Arnold Network based C3 module (RT-DETR-R18-KANC3) achieves 0.982 precision, 0.955 recall, and 0.964 mAP50 under low-contrast conditions, representing a 1% improvement over the baseline model and exceeding YOLO-series models by >40% in relative performance metrics. Full article

Rockfalls on railways are considered a natural disaster under the topic of landslides. It is an event that varies regionally due to landforms and climate. In addition to traffic density, the Karabük–Yenice railway line also passes through mountainous areas, river crossings, and experiences heavy seasonal rainfall. These conditions necessitate the implementation of proactive measures to mitigate risks such as rockfalls, tree collapses, landslides, and other geohazards that threaten the railway line. Undetected environmental events pose a significant threat to railway operational safety. The study aims to provide early detection of environmental phenomena using vibrations emitted through fiber optic cables. This study presents a real-time hazard detection system that integrates Distributed Acoustic Sensing (DAS) with a hybrid ensemble learning model. Using fiber optic cables and the Luna OBR-4600 interrogator, the system captures environmental vibrations along a 6 km railway corridor in Karabük, Türkiye. CatBoosting, Support Vector Machine (SVM), LightGBM, Decision Tree, XGBoost, Random Forest (RF), and Gradient Boosting Classifier (GBC) algorithms were used to detect the incoming signals. However, the Voting Classifier hybrid model was developed using SVM, RF, XGBoost, and GBC algorithms. The signaling system on the railway line provides critical information for safety by detecting environmental factors. Major natural disasters such as rockfalls, tree falls, and landslides cause high-intensity vibrations due to environmental factors, and these vibrations can be detected through fiber cables. In this study, a hybrid model was developed with the Voting Classifier method to accurately detect and classify vibrations. The model leverages an ensemble of classification algorithms to accurately categorize various environmental disturbances. The system has proven its effectiveness under real-world conditions by successfully detecting environmental events such as rockfalls, landslides, and falling trees with 98% success for Precision, Recall, F1 score, and accuracy. Full article

This study presents a stability assessment of rock slopes, considering the joint systems of the rock walls of Wojciech Bednarski Park. Special emphasis was placed on analysing the orientation and infill characteristics of the identified joint sets. Based on archival data and newly conducted geological surveys, stability calculations were performed for eight representative cross-sections corresponding to designated sectors. Numerical analyses were conducted using a finite element method (FEM) programme, based on the actual structure of the rock mass, specifically its discontinuities. This ensured a reliable reflection of the real conditions governing the slope instability mechanisms. Factors of safety were estimated with the Shear Strength Reduction Technique. The results indicate that slope failure is highly unlikely in Sectors 1 and 2 (FS > 1.50), unlikely but not fully meeting the safety criteria in Sector 3 (FS < 1.50), and highly probable in Sectors 4 and 6 (FS << 1.00), where unstable rock blocks and deeper structural slides are anticipated. In Sector 5, failure is considered probable (FS < 1.30) due to rockfalls, unstable blocks, and creeping weathered cover. For Sectors 7 and 8, assuming debris cover above the rock walls, failure is unlikely (FS > 1.50). In contrast, under the assumption of weathered material, it becomes probable in Sector 7 (FS < 1.30), and remains unlikely in Sector 8 (FS > 1.50). Due to the necessity of adopting several modelling assumptions, the results should be interpreted primarily in qualitative terms. The outcomes of this research provide a critical basis for assessing the stability of rock slopes within Wojciech Bednarski Park and support decision-making processes related to its planned revitalisation. Full article

Photogrammetry can be a valuable tool for understanding landscape evolution and natural hazards such as landslides. However, factors such as vegetation cover, shadows, and unstable ground can limit its effectiveness. Using photos across time to monitor an area with unstable or changing ground conditions results in fewer tie points between images across time, and often leads to low comparative accuracy if single-epoch (i.e., classical) photogrammetric processing approaches are used. This paper presents a study evaluating the co-alignment approach applied to fixed terrestrial timelapse photos at an active landslide site. The study explores the comparative accuracy of reconstructed surface models and the location and behavior of tie points over time in relation to increasing levels of global change due to landslide activity and rockfall. Building upon previous work, this study demonstrates that high comparative accuracy can be achieved with a relatively low number of inter-epoch tie points, highlighting the importance of their distribution across stable ground, rather than the total quantity. High comparative accuracy was achieved with as few as 0.03 percent of the overall co-alignment tie points being inter-epoch tie points. These results show that co-alignment is an effective approach for conducting change detection, even with large degrees of global changes between surveys. This study is specific to the context of geoscience applications like landslide monitoring, but its findings should be relevant for any application where significant changes occur between surveys. Full article

Understanding rockfall behavior is essential for accurately predicting hazards in both natural and engineered environments, yet prior research has predominantly focused on spherical rocks or single-impact scenarios, leaving critical gaps in highlighting the dynamics of non-spherical rocks and multiple impacts. This study addresses these shortcomings by investigating the influence of rock shape and release angle on motion, energy dissipation, and impact behavior. To achieve this, an innovative approach rooted in the Video-Assisted Rockfall Kinematics Analysis (VARKA) procedure was introduced, integrating a custom-designed apparatus, controlled experimental setups, and sophisticated data analysis techniques. Experiments utilizing a pendulum-based release system analyzed various scenarios involving different rock shapes and release angles. These tests provided comprehensive motion data for multiple impacts, including trajectories, translational and angular velocities, and the coefficient of restitution (COR). Results revealed that non-spherical rocks exhibited significantly more erratic trajectories and greater variability in COR values compared to spherical rocks. The experiments demonstrated that ellipsoidal and octahedral shapes had substantially higher variability in runout distances than spherical rocks. COR values for ellipsoidal shapes spanned a wide range, in contrast to the tighter clustering observed for spherical rocks. These findings highlight the pivotal influence of rock shape on lateral dispersion and energy dissipation, reinforcing the need for data-driven approaches to enhance and complement traditional physics-based predictive models. Full article

The stability of bottom structures in block caving mines is significantly challenged by impact loads generated from large rockfalls and ore collapses on slopes. This study aims to investigate the mechanical response and failure characteristics of bottom structures under such dynamic and cyclic loading conditions. Discrete element methods (DEMs) were employed to simulate the impact load amplitudes caused by large rockfalls on bottom structures. Specimens with identical mechanical properties to the bottom structure were fabricated at a 1:100 scale, based on the principle of similarity ratio tests. Three distinct types of impact loads were identified and analyzed: overall impact from large-scale slope collapses, localized impact from partial rock and soil mass collapses, and continuous multiple impacts from progressive slope failures. True triaxial tests were conducted to evaluate the mechanical response of the bottom structure under these loading scenarios. The results indicate that while overall and multiple impact loads from slope collapses do not lead to catastrophic failure of the bottom structure, severe damage occurs under a 100 m thickness of ore and large block impacts. Specifically, the inner walls of ore accumulation troughs peel off, and ore pillars between troughs fracture and fail. This study highlights the need for advanced experimental and numerical approaches to accurately predict the stability and failure modes of bottom structures under complex loading conditions. Full article

This study aims to mitigate slope-collapse hazards that threaten life and property at the Lujiawan resettlement site in Wanbi Town, Dayao County, Yunnan Province, within the Guanyinyan hydropower reservoir. It integrates centimeter-level point-cloud data collected by a DJI Matrice 350 RTK equipped with a Zenmuse L2 airborne LiDAR (Light Detection And Ranging) sensor with detailed structural-joint survey data. First, qualitative structural interpretation is conducted with stereographic projection. Next, safety factors are quantified using the limit-equilibrium method, establishing a dual qualitative–quantitative diagnostic framework. This framework delineates six hazardous rock zones (WY1–WY6), dominated by toppling and free-fall failure modes, and evaluates their stability under combined rainfall infiltration, seismic loading, and ambient conditions. Subsequently, six-degree-of-freedom Monte Carlo simulations incorporating realistic three-dimensional terrain and block geometry are performed in RAMMS::ROCKFALL (Rapid Mass Movements Simulation—Rockfall). The resulting spatial patterns of rockfall velocity, kinetic energy, and rebound height elucidate their evolution coupled with slope height, surface morphology, and block shape. Results show peak velocities ranging from 20 to 42 m s⁻¹ and maximum kinetic energies between 0.16 and 1.4 MJ. Most rockfall trajectories terminate within 0–80 m of the cliff base. All six identified hazardous rock masses pose varying levels of threat to residential structures at the slope foot, highlighting substantial spatial variability in hazard distribution. Drawing on the preceding diagnostic results and dynamic simulations, we recommend a three-tier “zonal defense with in situ energy dissipation” scheme: (i) install 500–2000 kJ flexible barriers along the crest and upper slope to rapidly attenuate rockfall energy; (ii) place guiding or deflection structures at mid-slope to steer blocks and dissipate momentum; and (iii) deploy high-capacity flexible nets combined with a catchment basin at the slope foot to intercept residual blocks. This staged arrangement maximizes energy attenuation and overall risk reduction. This study shows that integrating high-resolution 3D point clouds with rigid-body contact dynamics overcomes the spatial discontinuities of conventional surveys. The approach substantially improves the accuracy and efficiency of hazardous rock stability assessments and rockfall trajectory predictions, offering a quantifiable, reproducible mitigation framework for long slopes, large rock volumes, and densely fractured cliff faces. Full article