Search Results (47)

To address the rockburst hazard caused by overhanging hard roofs and difficult caving during the initial mining period of thick coal seam fully mechanized working faces, this study takes the N4202 fully mechanized top coal caving working face of the Santunzi Coal Mine as the field engineering background. The mined No. 4-1 coal seam has an average thickness of 9.46 m, and its overlying hard roof is composed of medium sandstone and siltstone. A total of 39 hydraulic fracturing boreholes, including type A, type B, type C1/C2, and fan-shaped holes, were deployed, with a designed fracturing depth of 19 m. Three testing means, including a CXK12(B) borehole imaging instrument, a KJ1222 microseismic monitoring system, and on-site roof caving observations, were adopted to comprehensively evaluate the field performance of roof hydraulic fracturing, and the rockburst prevention mechanism was analyzed. The field test results indicate that dense and well-connected fractures are formed after fracturing, with more than 8 fractures per single borehole and a fracture aperture of 0.8–2.2 mm, and the connectivity rate between adjacent fracturing boreholes reaches 92.3%. The initial mining top caving step distance of the working face is reduced to 13.2 m, while the theoretical calculated values are 10 m for the immediate roof and 15.6 m for the main roof. The roof gradually collapses, and the mining pressure is alleviated. During fracturing, the frequency and energy of microseismic events increase by 285% and 230%, respectively, compared to the state before fracturing. In the subsequent mining process, the maximum microseismic energy is only 4.56 kJ, which is far lower than the rockburst critical energy threshold (20 kJ) of this mine. Therefore, no rockburst hazard occurs in the working face. These research findings can provide a practical technical reference for rockburst prevention using hard roof hydraulic fracturing in similar thick coal seam fully mechanized mining faces. Full article

Mediterranean-type forest ecosystems are becoming increasingly vulnerable to intensifying drought, threatening the resilience of even highly adapted ecosystems such as the Northern Jarrah Forest in south-western Australia. This study quantifies multi-decadal dynamics of canopy water stress using a 36-year multispectral satellite archive (1988–2024) and the newly developed Infrared Canopy Dryness Index (ICDI). We combined this spatiotemporal dataset with a MaxEnt-based risk assessment framework to identify the biophysical drivers of drought-induced canopy loss and to delineate high-risk zones under accelerating climate-forcing changes. Our results demonstrate a systematic spatial expansion of canopy dryness, paralleling a deteriorating regional climatic water balance. Hotspot analysis revealed a transition from localized, peripheral stress to widespread, chronic drought conditions across the landscape. The modelling achieved high diagnostic accuracy (AUC = 0.952), significantly outperforming conventional assessment methods. Regolith depth was identified as the primary determinant of drought-induced canopy collapse, followed by ICDI, NDVI, and slope. Crucially, high-biomass stands exhibited disproportionately higher risk of collapse, revealing a density-dependent vulnerability that suggests productive forests are approaching critical hydraulic thresholds. Conversely, lower-stature forests to the east of the study area demonstrated greater stability, likely due to reduced evapotranspirative demand. These findings provide robust spatial evidence for transitioning from reactive monitoring to proactive forest management. We conclude that targeted interventions, such as ecological thinning and prescribed burning in identified high-risk zones, are imperative to protect the forest and preserve the structural integrity of Mediterranean ecosystems in a drying climate. Full article

Given the great burial depth, ancient depositional age, and multi-phase tectonic evolution of deep formations, drilling operations are highly susceptible to wellbore instability. The design and deployment of directional wells further exacerbate this risk, underscoring the need for quantitative risk assessments for directional drilling operations. Based on linear poroelasticity theory, a mechanical model for directional wellbore stability is established to enable wellbore stability evaluation and trajectory optimization design. Furthermore, an intelligent prediction method for collapse pressure is proposed using the XGBoost algorithm. The results indicate that the prediction accuracy of collapse pressure reaches 93%. Under strike-slip in situ stress regimes, wellbore stability is most critical for vertical wells, whereas horizontal and directional wells exhibit lower collapse pressure. The optimal wellbore trajectory is determined to be a horizontal well with an azimuth approximately 36° deviated from the maximum horizontal principal stress direction. The intelligent prediction results show a 98% goodness-of-fit with theoretical calculations, reducing the calculation time from hours to seconds. This study provides a novel approach for wellbore stability analysis and offers a practical tool for the rapid risk assessment of wellbore collapse during directional drilling operations. Full article

The initiation of the caving process in block/panel caving is critical to the success of mines. However, there is no widely adopted methodology for modeling the onset of caving. This study proposes a methodology to model the initial stages of caving using the Discrete Element Method, in which the rock mass is represented using the Bonded Particle Model, and the undercut material is modeled with non-cohesive discrete particles. The collapse of the rock mass was replicated following a parameter calibration process, and the results were compared with actual mining data of the observed initial fragmentation. Key parameters were identified, such as allowable normal and shear stresses, which are essential to accurately represent the collapse of the rock mass and the evolution of the early stage of rock fragmentation. Low allowable stress values led to premature collapse and finer fragmentation, whereas higher values delayed cave back failure and resulted in coarser initial fragmentation. The results showed the formation of large rock fragments between 14 and 15 m during the initial cave back failures. Subsequently, larger fragments ranging from 2 to 9 m were observed detaching from the cave back as draw progressed, with sizes comparable to those reported in some block/panel caving operations. The main contribution of this work is a methodology that demonstrates the feasibility of modeling caving initiation, which is crucial in a context where increasing rock mass strength and deposit depth require design changes at the production level and pose significant uncertainty in the rock mass response. Full article

Non-cohesive earth dams are widely distributed in natural and semi-engineering scenarios, and overtopping-induced breaches are their most catastrophic failure mode. Accurate prediction of the overtopping failure process and breach evolution is critical for risk assessment, emergency management, and dam design optimization. In this study, an improved 3D numerical method is developed to simulate the coupled hydrodynamic–erosion–breach evolution processes of non-cohesive earth dams. The model based on the finite volume method integrates three core modules: a hydrodynamic module based on the Reynolds-Averaged Navier–Stokes equations with the Volume of Fluid method for free surface tracking, a dam material erosion module considering particle entrainment and transport mechanisms of non-cohesive soils, and a breach development module coupling erosion and gravitational collapse. To validate the model, two levels of verification are conducted: first, a classic benchmark dam break case is employed to confirm the feasibility of the hydrodynamic and breach evolution algorithms; second, published flume experimental data of non-cohesive earth dam overtopping failures are adopted to evaluate the model accuracy in predicting breach hydrographs and spatiotemporal evolution of breach geometry. The results demonstrate that the proposed model accurately reproduces the key characteristics of overtopping failure with high fidelity. The predicted breach flow rates and flow depths are in excellent agreement with experimental observations, with relative errors less than 5% for both peak discharge and time to peak. Consequently, this study provides a reliable numerical tool for detailed simulation of non-cohesive earth dam breaches and offers scientific support for emergency management. Full article

Soft-ground-story configurations in high-rise buildings present a critical vulnerability during seismic events, often leading to disproportionate structural damage and collapse. This study focuses on the systematic identification of soft-ground-story high-rise structures in Bucharest, a city located in a high seismic hazard zone influenced by Vrancea intermediate-depth earthquakes. The research employs a multi-step methodology combining field surveys, structural documentation, and analysis of architectural layouts from various sources to detect soft-ground-story irregularities across the urban building stock in Bucharest. The findings reveal that such configurations remain prevalent in mixed-use structures along major boulevards, where open ground floors were historically favoured for commercial purposes. The results provide a database of soft-ground-story high-rise buildings in Bucharest, highlighting their prevalence in distinct urban districts and their potential impact on seismic risk. Quantitative screening indicators, vertical element area ratio and mean axial stress in ground-story columns, are proposed for rapid vulnerability assessment. Dynamic measurements confirm a 33–38% increase in fundamental eigenperiods after the 1977 earthquake, indicating moderate-to-extensive damage states. These findings underscore the urgent need for targeted retrofitting strategies and inform seismic risk mitigation policies. The study provides a foundation for future integration of advanced diagnostic tools, such as image-based deep learning and vibration monitoring, into citywide seismic resilience planning. Full article

Red–Green–Blue–Thermal (RGBT) object detection is critical for robust all-weather perception. However, deploying dual-stream networks on resource-constrained edge devices is severely hindered by insufficiently adaptive multimodal fusion, the loss of small-object features during downsampling, and substantial computational overhead. To address these challenges, we propose GEM-YOLO, a real-time and lightweight RGBT detector. Specifically, an Adaptive Multimodal Gated Fusion Mechanism (GFM) is designed to dynamically calibrate modality weights and suppress noise. Furthermore, Space-to-Depth (SPD) convolutions are integrated into the backbone to achieve lossless downsampling, preventing the feature collapse of small targets. Finally, a lightweight Ghost-Neck is constructed using Ghost modules and GSConv to eliminate computational redundancy. Extensive experiments on the Forward-Looking Infrared (FLIR) and Multi-Modal Multispectral Fusion Dataset (M3FD) datasets demonstrate the effectiveness of the proposed method. With only 7.58 Giga Floating-Point Operations (GFLOPs) and 3.44 million parameters (M), GEM-YOLO reduces the computational cost by 18.6% relative to the dual-stream YOLOv11n baseline. Concurrently, it achieves competitive mean Average Precision at IoU = 0.5 (mAP@50) scores of 82.8% and 69.0% on FLIR and M3FD, respectively, with more evident gains on small-target localization. In practice, GEM-YOLO maintains competitive detection performance while keeping computational overhead low, making it promising for real-time multispectral perception on resource-constrained edge platforms. Full article

The rapid advancement of automated essay scoring (AES) has been constrained by a representation bottleneck, where monolithic models collapse diverse facets of writing constructs into a single, uninterpretable signal, undermining the pedagogical value of multi-dimensional rating traits. To address this limitation, the RoBERTa-based Trait-Aware Transformer (RoBERTa-TAT) is introduced. This architectural reframing replaces unified pooling with parallel, trait-specific attention streams, preserving and disentangling critical features such as conceptual depth and mechanical precision. Tested on the ASAP Dataset-7, RoBERTa-TAT attains a new state-of-the-art Quadratic Weighted Kappa (QWK) of 0.936, outperforming sequential baselines and conventional Transformer variants. Beyond gains in accuracy, this trait-specialized architecture recasts scoring from a black-box prediction into a transparent diagnostic tool, enabling actionable, fine-grained feedback at different rating traits. High-resolution inspection reveals that the model’s internal representations correlate with specific linguistic markers—such as discourse connectives for organization—suggesting a degree of structural alignment with expert judgment. By aligning high-capacity representation learning with the granular demands of formative assessment, RoBERTa-TAT provides a practical, interpretable blueprint for deploying accountable AI in education and broadening access to expert diagnostic insight. Full article

Cavitation erosion is a critical problem for many engineering components, such as ship propellers, diesel engine exhaust valves, cylinder liners, pump impeller blades, hydraulic turbines, and bearings, which are exposed to high-velocity flowing fluids or to vibratory fluid motion. It represents a mechanical degradation of the surface caused by the continuous collapse of bubbles in the surrounding liquid, which seriously affects flow efficiency and component service life, increasing maintenance frequency and refurbishment costs. The intensity and evolution of the cavitation erosion phenomenon depend on the hydrodynamic conditions to which the component surface is exposed, the properties of the liquid, and the judicious selection of the most suitable material. This paper aims to modify the microstructure of a Ni-based superalloy by applying recrystallization annealing heat treatment in order to obtain surfaces resistant to cavitation erosion for components that handle fluids under local pressure fluctuations. Experimental tests are carried out using a vibratory apparatus with piezoceramic crystals operating at a frequency of 20 kHz and an amplitude of 50 µm. The cavitation erosion performance of the Ni-based superalloy INCONEL 625, heat treated by recrystallization annealing, are compared with that of austenitic stainless steel AISI 316L subjected to solution treatment. For both metallic alloys, based on mass loss measurements, the characteristic time-dependent curves of the mean cumulative erosion penetration depth, MDE(t), and the mean erosion rate, MDER(t), are determined. The comparison of these curves and of the parameters defined and recommended by the ASTM G32 standard demonstrates that, for the Inconel 625 superalloy, resistance to cavitation erosion increases by 77–81% compared to that of AISI 316L austenitic stainless steel. X-ray diffraction analyses (XRD) show that, in the microstructure of the Inconel 625 superalloy, in addition to austenite, MC-type carbides, M₂₃C₆ carbides, and intermetallic phases γ″ = Ni₃(Nb, Al, Ti) and δ = Ni₃(Nb, Mo) are also present. Full article

The rapid development of unmanned aerial vehicle (UAV) technology necessitates reliable recognition methods. Radio frequency (RF)-based recognition is promising, but conventional real-valued CNNs (RV-CNNs) typically discard phase information from RF spectrograms, leading to degraded performance under low-signal-to-noise ratio (SNR) conditions. To address this, this paper proposes a complex-valued CNN (CV-CNN) that operates on a constructed complex representation, where the real part is the logarithmic power spectral density (PSD) and the imaginary part is derived from Sobel edge detection. This enables genuine complex convolutions that fuse magnitude and structural cues, enhancing noise resilience. As complex-valued networks are known to be sensitive to architectural choices, we conduct comprehensive ablation experiments to investigate the impact of key hyperparameters on model performance, revealing critical stability constraints (e.g., performance collapse beyond 4–5 network depth). Evaluated on the 25-class DroneRFa dataset, the proposed model achieves 100.00% accuracy under noise-free conditions. Crucially, it demonstrates significantly superior robustness in low-SNR regimes: at −20 dB SNR, it attains 15.58% accuracy, over seven times higher than a dual-channel RV-CNN (2.20%) with identical inputs; at −15 dB, it reaches 45.86% versus 14.03%. These results demonstrate that the CV-CNN exhibits potentially superior robustness and interference resistance in comparison to its real-valued counterpart, maintaining high recognition accuracy even under low-SNR conditions. Full article

The long-term stability of legacy pillars remains a critical challenge in mining engineering, as pillar collapse may threaten human safety, damage infrastructure, and complicate sustainable mine closure. Conventional empirical methods are often inadequate for addressing the complexity of heterogeneous rock masses and time-dependent deterioration. In this study, a dual-perspective framework is proposed by integrating finite difference method (FDM)-based numerical simulation with artificial intelligence (AI) techniques to improve the reliability of pillar safety assessment. FDM models are developed to analyze stress redistribution, deformation, and failure processes of pillars under varying depths, geometries, rock quality, and rock mechanics. In parallel, AI models are trained on datasets derived from numerical simulations to provide rapid predictions of pillar instability probability (P_f) with high computational efficiency. The complementary use of both approaches ensures cross-validation: FDM simulations provide mechanistic insights into pillar behavior, while AI models enhance predictive capability and account for uncertainties in geological conditions. The integrated framework demonstrates superior robustness and applicability compared to single-method approaches, offering a comprehensive tool for assessing legacy pillar safety. This research provides practical guidance for hazard mitigation, mine closure planning, and the development of monitoring strategies in sustainable mining engineering. Full article

Wellbore instability is a critical challenge in deep coalbed methane (CBM) development, especially for extended-reach horizontal wells subjected to pronounced horizontal in situ stress anisotropy. This study integrates uniaxial and triaxial laboratory testing of deep coal samples with an analytical Mohr–Coulomb-based model to quantify how horizontal stress contrast redistributes near-wellbore stresses and controls collapse pressure. Mechanical parameters from core experiments and log-derived stresses are embedded into the model and applied to six representative horizontal wells in the Ordos Basin. At 2000 m depth, circumferential stress perpendicular to the minimum horizontal stress direction exceeds orthogonal directions by 20 MPa (wells 1–3) and 40–50 MPa (wells 4–6). As the horizontal stress ratio n = ${\sigma }_H$/${\sigma }_h$ (where ${\sigma }_H$ and ${\sigma }_h$ are the maximum and minimum horizontal in situ stresses, respectively) increases from 1.07 to 1.28, the equivalent mud density required to prevent collapse rises from 1.53 to 1.77–1.81 g/cm³, representing a 15–18% increase. These results demonstrate that explicitly accounting for horizontal stress anisotropy—calibrated by uniaxial and triaxial tests—is essential for reliable collapse-pressure estimation in extended-reach wells drilled in deep coal seams, without invoking additional trajectory-optimization assumptions. Full article

Collapsibility is a fundamental geotechnical property of loess that critically affects its engineering behavior. In this study, a comprehensive dataset comprising 9041 experimental records on the physical properties and collapsibility of loess from the Xi’an region was compiled. Six parameters were selected as model inputs: sampling depth (H), water content (w), plastic limit (w_P), plasticity index (I_P), compression coefficient (a_1–2), and compression modulus (E_s). Based on these inputs, prediction models for the loess collapsibility coefficient (δ_s) were developed using Gaussian Process Regression (GPR), Gradient Boosting Machine (GBM), Support Vector Regression (SVR), Radial Basis Function Neural Network (RBFNN), Classification and Regression Tree (CART), and Feature Tokenizer Transformer (FT-Transformer). Among these, GPR demonstrated the best predictive performance, achieving the lowest error (RMSE = 9.88 × 10⁻³) and the highest accuracy (R² = 0.844). Additionally, the coverage proportion of the 95% confidence interval of the GPR predictions reached 0.949. SHapley Additive exPlanations (SHAP) analysis for GPR further revealed that the compression coefficient exerted the greatest influence on δ_s (0.0149), followed by compression modulus (0.0080), water content (0.0068), plasticity index (0.0061), sampling depth (0.0061), and plastic limit (0.0052). The GPR-based prediction model offers significantly higher predictive accuracy than empirical models. The developed models provide a robust technical framework for the rapid estimation of loess collapsibility in the Xi’an region. Full article

Understanding mineralogical transformations and elemental mobility during limestone weathering is critical for deciphering carbon cycling and critical zone evolution in karst terrains. This study investigates an in situ limestone weathering profile (12.6 m depth) in Shimen, Hunan Province, using integrated mineralogical (XRD, EPMA-EDS), elemental (XRF, ICP-MS), and Sr isotopic (MC-ICP-MS) analyses. Results reveal a two-stage pedogenic model: (1) Rapid dissolution of primary calcite (>95 wt% in bedrock to 1.1–48.5 wt% in soil) creates an abrupt bedrock–soil interface via volumetric collapse (>90%), accumulating acid-insoluble residues (quartz-dominated); (2) Subsequent weathering drives illitization of K-feldspar, trace element enrichment (e.g., Ni, Tl, Th τ up to 180) via illite adsorption, and radiogenic ⁸⁷Sr/⁸⁶Sr evolution (0.7076 in bedrock to 0.7292 in soil). Depth-dependent increases in chemical index of alteration (CIA: 6.79–79.96) and mass transfer coefficients confirm progressive weathering intensity. The profile acts as a net carbon source (58.5% depletion in soil inorganic carbon), highlighting significant CO₂ release during pedogenesis. These findings provide mechanistic insights into subtropical critical zone evolution and element cycling in carbonate-dominated systems. Full article

Progressive collapse of building structures induced by accidental extreme loads has garnered significant attention. This study aimed to assess the impact resistance of steel-framed subassemblies with extended reverse channel connections under falling debris impact. It also sought to provide technical support for anti-collapse design. Drop-hammer impact tests were conducted to obtain baseline data. A validated finite element model using ANSYS/LS-DYNA was employed for the parametric analyses. The key parameters investigated included the impact location (mid-span vs. beam end), falling height of the impactor, and span-to-depth ratio of steel beams, with a focus on the impact resistance. The results reveal that the impact resistance depends on both the peak load capacity and the deformation capacity. The mid-span impacts exhibited higher resistance at falling heights ≥ 1.0 m due to greater plastic deformation. In contrast, the beam-end impacts performed better when the falling heights were ≤0.5 m. The impact resistance decreased with an increasing falling height. The reduction ratios exceeded the theoretical values due to the post-impact gravitational energy input. Smaller SDRs enhanced the peak resistance under both impact scenarios, with more pronounced effects in the mid-span cases. Catenary action significantly improved the mid-span impact resistance (19.3–66.7%). However, it contributed minimally to the beam-end impact resistance (0.61–1.09%), where shear action dominated. These findings offer critical technical support for optimizing steel structure designs to resist falling debris impact and enhance overall structural robustness. Full article