Search Results (494)

The repeated extraction of coal seams in the Loess Plateau mining region has greatly increased the severity of surface deformation and associated damage. Accurately characterizing the spatio-temporal evolution of subsidence and the underlying mechanisms is a critical engineering challenge for mining safety. Taking the Dafosi Coal Mine located in the loess gully region as a case study, this paper thoroughly examines the variations in surface deformation and damage characteristics caused by single and repeated seam mining. The analysis integrates surface movement monitoring data, global navigation satellite system (GNSS) dynamic observations, field surveys, unmanned aerial vehicle (UAV) photogrammetry, and numerical simulation methods. Notably, to ensure the accuracy of prediction parameters, a refined Particle Swarm Optimization (PSO) algorithm incorporating a neighborhood-based mechanism was employed specifically for the inversion of probability integral parameters. The results indicate that the subsidence factor and horizontal movement factor increase markedly following repeated mining. The maximum surface subsidence velocity also increases substantially, and this acceleration remains evident after normalizing by mining thickness and face-advance rate. The fore effective angle is smaller in repeated mining than in single-seam mining, and the duration of surface movement is substantially extended. Repeated mining fractured key strata and caused a functional transition from the classic three-zone response to a two-zone connectivity pattern, while the thick loess cover responds as a disturbed discontinuous-deformation layer, which together aggravates step-like and slope-related damage. The severity of surface damage is strongly influenced by topographic features and geotechnical properties. These findings demonstrate that the proposed integrated approach is highly effective for geological hazard assessment and provides a practical reference for engineering applications in similar complex terrains. Full article

Landslide deformation monitoring plays a critical role in geohazard prevention and risk mitigation in mountainous regions, where timely and reliable deformation information is essential for early warning and disaster management. Monitoring landslide deformation in mountainous areas remains a persistent challenge, largely due to rugged topography, dense vegetation cover, and low interferometric coherence—factors that substantially limit the effectiveness of conventional InSAR methods. To address these issues, this study aims to develop a robust time-series InSAR framework for enhancing deformation detection and measurement density under low-coherence conditions in complex mountainous terrain, and accordingly introduces the Sequential Estimation and Total Power-Enhanced Expectation–Maximization Inversion (SETP-EMI) approach, which integrates dual-polarization Sentinel-1 SAR time series within a recursive estimation framework, augmented by polarimetric coherence optimization. This methodology allows for dynamic assimilation of SAR data, improves phase quality under low-coherence conditions, and enhances the extraction of distributed scatterers (DS). When applied to Zhenxiong County, Yunnan Province—a region prone to geohazards with complex terrain—the SETP-EMI method achieved a landslide detection rate of 94.1%. It also generated approximately 2.49 million measurement points, surpassing PS-InSAR and SBAS-InSAR results by factors of 22.5 and 3.2, respectively. Validation against ground-based leveling data confirmed the method’s high accuracy and robustness, yielding a standard deviation of 5.21 mm/year. This study demonstrates that the SETP-EMI method, integrated within a DS-InSAR framework, effectively overcomes coherence loss in densely vegetated plateau regions, improving landslide monitoring and early-warning capabilities in complex mountainous terrain. Full article

The challenge of insufficient monitoring accuracy in vision-based multi-point displacement measurement of bridges using Unmanned Aerial Vehicles (UAVs) stems from camera motion interference and the limitations in camera performance. Existing methods for UAV motion correction often fall short of achieving the high precision necessary for effective bridge monitoring, and there is a deficiency of high-performance cameras that can function as adaptive sensors. To address these challenges, this paper proposes a UAV vision-based method for multi-point displacement measurement of bridges and introduces a monitoring system that includes a UAV-mounted camera, a computing terminal, and targets. The proposed technique was applied to monitor the dynamic displacements of the Lunzhou Highway Bridge in Qingyuan City, Guangdong Province, China. The research reveals the deformation behavior of the bridge under vehicle traffic loads. Field test results show that the system can accurately measure vertical multi-point displacements across the entire span of the bridge, with monitoring results closely matching those obtained from a Scheimpflug camera. With a root mean square error (RMSE) of less than 0.3 mm, the proposed method provides essential data necessary for bridge displacement monitoring and safety assessments. Full article

Terrace construction is a critical engineering practice for soil and water conservation as well as sustainable agricultural development on the Loess Plateau (LP), China, where high-precision dynamic monitoring is essential for informed regional ecological governance. To address the challenges of inadequate extraction accuracy and poor model generalization in time-series terrace mapping amid complex terrain and spectral confounding, this study proposes a novel Swin Transformer-based Terrace Dual-Branch Deformable Boundary Network (STDBNet) that seamlessly integrates multi-source remote sensing (RS) data with deep learning (DL). The STDBNet model integrates the Swin Transformer architecture with a dual-branch attention mechanism and introduces a boundary-assisted supervision strategy, thereby significantly enhancing terrace boundary recognition, multi-source feature fusion, and model generalization capability. Leveraging Sentinel-2 multi-temporal optical imagery and terrain-derived features, we constructed the first 10-m-resolution spatiotemporal dataset of terrace distribution across the LP, encompassing nine annual periods from 2017 to 2025. Performance evaluations demonstrate that STDBNet achieved an overall accuracy (OA) of 95.26% and a mean intersection over union (MIoU) of 86.84%, outperforming mainstream semantic segmentation models including U-Net and DeepLabV3+ by a significant margin. Further analysis reveals the spatiotemporal evolution dynamics of terraces over the nine-year period and their distribution patterns across gradients of key terrain factors. This study not only provides robust data support for research on terraced ecosystem processes and assessments of soil and water conservation efficacy on the LP but also lays a scientific foundation for informing the formulation of regional ecological restoration and land management policies. Full article

Land degradation in drylands increasingly threatens infrastructure and the performance of renewable energy (RE) systems through coupled hydro-chemo-mechanical changes in soil fabric, density, matric suction, and pore–water chemistry. A key gap is the limited integration of unsaturated soil mechanics with practical indicator sets used in engineering screening and operations. This narrative review synthesizes evidence from targeted searches of Scopus, Web of Science, and Google Scholar. Searches are complemented by key organizational reports and standards, as well as citation tracking. Priority is given to sources that report mechanisms linked to measurable indicators, thresholds, tests, or models relevant to dryland infrastructure. The synthesis uses the soil-water characteristic curve (SWCC) and hydraulic conductivity k(θ) to connect hydraulic state to strength and deformation and couples these with chemical indices, including electrical conductivity (EC), exchangeable sodium percentage (ESP), and sodium adsorption ratio (SAR). Practical diagnostics include the dynamic cone penetrometer (DCP) and California Bearing Ratio (CBR) tests, infiltration and crust-strength tests, monitoring with unmanned aerial vehicles (UAVs), geophysics, and in situ moisture and suction sensing. The contribution is an indicator-driven, practice-oriented framework linking mechanisms, monitoring, and mitigation for photovoltaic (PV), concentrating solar power (CSP), wind, transmission, and well-pad corridors. This framework is implemented by consistently linking unsaturated soil state (SWCC, k(θ), and matric suction) to degradation processes, measurable indicator/test sets, and trigger-based interventions across the review. Full article

As a critical indicator of polar ecosystem dynamics, sea ice monitoring plays a pivotal role in climate change. However, as global warming accelerates the melting of sea ice, the complexity in the Arctic poses growing challenges for achieving high-precision sea ice classification. To address this issue, this study begins with the creation of a multi-source sea ice dataset based on GaoFen-3 fully polarimetric SAR data and Landsat optical imagery. In addition, the study proposes a Global–Local enhanced Deformable Convolution Network (GLDCN), which effectively captures long-range semantic dependencies and fine-grained local features of sea ice. To further enhance feature integration, an Adaptive Channel Attention Module (ACAM) is designed to achieve adaptive weighted fusion of heterogeneous SAR and optical features, substantially improving the model’s discriminative ability in complex conditions. Experimental results show that the proposed method outperforms several mainstream models on multiple evaluation metrics. The multi-source data fusion strategy significantly reduces misclassification among confusable categories, validating the importance of multimodal fusion in sea ice classification. Full article

Thermal gradients induced by friction frequently trigger buckling deformation of the friction elements, especially in heavy-duty helicopters. Nevertheless, the subsequent influence of such post-buckling deformation on transient thermal characteristics during helicopter successive shifting remains insufficiently addressed in existing research. In the present work, a gap model for friction pairs with conical separate discs is first proposed. Subsequently, a comprehensive thermal-fluid-dynamic model incorporating spline friction, split springs, and time-varying thermal parameters is developed to investigate the transient thermal characteristics of wet clutches with conical separate discs in successive shifting. A corresponding qualitative analysis is performed to explore the transient thermal response and influence mechanisms of operating parameters, including shifting interval, rotation speed and control oil pressure. The results indicate that a rise in the control oil pressure from 1.5 MPa to 1.9 MPa facilitates a 42.65% increase in the maximum radial temperature gradient and augments the maximum axial temperature gradient by 24.35%. Meanwhile, an increase in rotation speed accelerates heat dissipation but compromises the uniformity of the temperature field. Additionally, extended shifting intervals under inadequate heat dissipation exacerbates thermal buildup, driving a persistent and significant escalation in the temperature of friction elements. The conclusions can provide a theoretical basis for the optimal design, condition monitoring, and fault diagnosis of aviation clutches. Full article

The dynamic characteristics of flotation foam, such as velocity and breakage rate, are critical factors that influence mineral separation efficiency. However, challenges inherent in foam images, including weak textures, severe deformations, and motion blur, present significant technical hurdles for dynamic monitoring. These issues lead to a fundamental conflict between the efficiency and accuracy of traditional feature matching algorithms. This paper introduces a novel progressive framework for dynamic feature matching in flotation foam images, termed “stable extraction, efficient coarse screening, and precise matching.” This framework first employs the Accelerated-KAZE (AKAZE) algorithm to extract robust, scale- and rotation-invariant feature points from a non-linear scale-space, effectively addressing the challenge of weak textures. Subsequently, it innovatively incorporates the Grid-based Motion Statistics (GMS) algorithm to perform efficient coarse screening based on motion consistency, rapidly filtering out a large number of obvious mismatches. Finally, the Progressive Sample and Consensus (PROSAC) algorithm is used for precise matching, eliminating the remaining subtle mismatches through progressive sampling and geometric constraints. This framework enables the precise analysis of dynamic foam characteristics, including displacement, velocity, and breakage rate (enhanced by a robust “foam lifetime” mechanism). Comparative experimental results demonstrate that, compared to ORB-GMS-RANSAC (with a Mean Absolute Error, MAE of 1.20 pixels and a Mean Relative Error, MRE of 9.10%) and ORB-RANSAC (MAE: 3.53 pixels, MRE: 27.36%), the proposed framework achieves significantly lower error rates (MAE: 0.23 pixels, MRE: 2.13%). It exhibits exceptional stability and accuracy, particularly in complex scenarios involving low texture and minor displacements. This research provides a high-precision, high-robustness technical solution for the dynamic monitoring and intelligent control of the flotation process. Full article

This paper presents a novel approach using machine learning methods for the automated segmentation of acceleration signals measured during ball burnishing (BB) operations performed on a computer numerical controlled (CNC) milling machine. The study addresses the challenge of accurately finding actual feedrates in that finishing operation, which often deviate from programmed values due to various dynamic reasons. The method involves a two-stage process: first, an automatic signal segmentation algorithm employing Gaussian Mixture Modeling (GMM) and K-means clustering is applied to the ball burnishing (BB) process and acceleration data. Second, a Taguchi L9 experimental design is used to assess the influence of some regime parameters on the actual feedrate and the BB’s cycle duration. Results show successful segmentation of the toolpaths based on X-axis accelerations and deforming force data, with the Calinski–Harabasz Index confirming good cluster separability. Programmed feedrate and the number of toolpath points were identified as the most significant factors affecting the percentage delay between programmed and obtained feedrates. The main contribution is the development and testing of a new method for segmenting different toolpath states in ball burnishing operations, based on measured accelerations and momentary deforming force magnitudes. The present work offers valuable insights into autonomous monitoring and control in BB operations. Full article

Effective post-disaster management requires continuous and reliable monitoring of the evolving disaster situation. While remote sensing provides objective measurements of ground deformation, social media data offer dynamic insights into public perception and disaster progression. However, integrating these complementary data sources to achieve sustained monitoring of disaster remains a challenge. To address this, we propose a novel framework that combines Sentinel-1 SAR data with Sina Weibo posts to improve dynamic earthquake impact assessment. Physical damage was quantified using D-InSAR-derived deformation. Disaster-related locations were identified using a fine-tuned pre-trained language model, and public sentiment was inferred through prompt-based few-shot learning with a large language model. Spatiotemporal analysis was performed to examine the relationship between sentiment dynamics and varying levels of physical damage, followed by an analysis of topic transitions within regional semantic networks to compare discussion patterns across areas. A case study of the 2023 Jishishan earthquake demonstrates the framework’s capability to continuously track disaster evolution: regions experiencing severe physical damage exhibit clear concentrations of negative sentiment, whereas increases in positive sentiment coincide with areas where rescue operations are effectively underway. These findings indicate that integrating the two data sources improves continuous disaster monitoring and situational awareness, thereby supporting emergency response. Full article

Dynamic loads on a vehicle’s running gear generated when driving over uneven roads or surfaces have a destructive effect on its components and, consequently, on the vehicle’s reliability. Special vehicles, especially off-road vehicles, are operated differently from traditional vehicles. Deformable surfaces can induce significant dynamic loads on vehicle running gear components even at low speeds, significantly limiting safe driving speeds. This article presents experimental vehicle tests conducted on four test track sections at three predefined vehicle speeds (10, 20, and 30 km/h). The experimental results demonstrate a clear dependence of dynamic loads on the off-road vehicle’s speed on dirt surfaces. Differences were observed between the measurement sections, suggesting that standard road profile metrics (e.g., RMS (Root Mean Square) profile height change) do not fully predict actual loads, requiring continuous monitoring of vehicle operating conditions. Compared to paved roads, where loads are more predictable, ground surfaces generate unique vibration patterns even at low driving speeds. RMS values for the measurement sections ranged from 0.02 to 0.06 m. Therefore, it is necessary to adapt test methods to specific ground conditions, with driving speed as a key research parameter. Full article

To study the mechanical properties and displacement evolution of rock masses near coal-seam normal faults under mining disturbances; this paper utilizes fiber optic monitoring and distributed strain measurement techniques to achieve the fine monitoring of the entire process of stress–displacement–strain during mining. The experimental design adopts a stepwise mining approach to systematically reproduce the evolution of fault formation; slip; and instability. The results show that the formation of normal faults can be divided into five stages: compressive deformation; initiation; propagation; slip; and stabilization. The strength of the fault plane is significantly influenced by the dip angle. As the dip angle increases from 30° to 70°, the peak strength decreases by 23%, and the failure mode transitions from tensile failure to shear failure. Under mining disturbances, the stress field in the overlying rock shifts from concentration to dispersion, with a stress mutation zone appearing in the fault-adjacent area. During unloading, vertical stress decreases by 45%, followed by a rebound of 10% as mining progresses. The rock layers above the goaf show significant subsidence, with the maximum vertical displacement reaching 150 mm. The displacement between the hanging wall and footwall differs, with the maximum horizontal displacement reaching 78 mm. The force chain distribution evolves from being dominated by compressive stress to a compressive–tensile stress coupling state. The fault zone eventually enters a stress polarization state and tends toward instability. A large non-uniform high-speed zone forms at the fault cutting point in the velocity field, revealing the mechanisms of fault instability and the initiation of dynamic disasters. These experimental results provide a quantitative understanding of the multi-physics coupling evolution characteristics of coal-seam normal faults under mining disturbances. The findings offer theoretical insights into the instability of coal-seam normal faults and the mechanisms behind the initiation of dynamic disasters. Full article

Underground coal mines are widely distributed across China, and underground mining is highly concealed. The rapid and accurate identification of the spatial distribution of coal mining subsidence over large areas is of significant importance for the reuse of land resources in mining areas and the detection of illegal mining activities. The traditional method of monitoring subsidence basins has limitations in terms of monitoring range and timeliness. The development of synthetic aperture radar (InSAR) technology has provided a valuable tool for monitoring mining subsidence areas. However, this method faces challenges in quickly and effectively monitoring subsidence basins using wide-swath SAR images. With the rapid development of deep learning and computer vision technologies, leveraging advanced deep learning models in combination with InSAR technology has become a crucial research direction to enhance the monitoring efficiency of surface subsidence in mining areas. Therefore, this paper proposes a new method for the rapid identification of mining subsidence basins in mining areas, which integrates Deformable Detection Transformer (Deformable DETR) and InSAR technology. First, the real deformation sample set of the mining area, obtained through interference processing, is combined with simulated deformation samples generated using the dynamic probability integral method, elastic transformation, and various noise synthesis techniques to construct a mixed InSAR sample set. This mixed sample set is then used to train the Deformable DETR model and compared with common deep learning methods. The experimental results show that the monitoring accuracy is significantly improved, with the model achieving a Precision of 0.926, Recall of 0.886, F1-score of 0.905, and mean Intersection over Union (mIoU) of 0.828. The detection model was applied to monitor the dynamically updated mining subsidence in the Huainan mining area from 2023 to 2024, detecting 402 subsidence basins. Further training demonstrates that the model exhibits strong robustness. Therefore, this method reduces the construction cost of the target detection training set and holds significant application potential for monitoring geological disasters in large-scale mining areas. Full article

Additive manufacturing (AM) with clay and ceramic-based materials is gaining momentum as a sustainable alternative in construction, yet its advancement depends on bridging experimental practice with predictive modeling. This review synthesizes advances in mathematical formulations and numerical tools applied to clay, geopolymers, alumina, and related extrusion-based pastes. Classical rheological models, including the Bingham and Herschel–Bulkley formulations, remain central for characterizing yield stress, structuration, and flow stability. Meanwhile, finite element (FEM) and computational fluid dynamics (CFD) approaches are increasingly supporting predictions of deformation, shrinkage, drying, and sintering. Despite these advances, their application to natural clay systems remains limited due to heterogeneity, moisture sensitivity, and the lack of standardized constitutive parameters. Recent studies emphasize that validation is essential: rheometry, layer stability tests, in situ monitoring, and prototyping provide necessary calibration for reliable simulation. In parallel, parametric and generative design workflows, particularly through Rhino and Grasshopper ecosystems, illustrate how digital methods can link geometric logic, fabrication constraints, and performance criteria. Overall, the literature demonstrates a transition from isolated modeling efforts toward integrated, iterative frameworks where rheology, numerical simulation, and experimental validation converge to improve predictability, reduce trial-and-error, and advance scalable and sustainable clay- and ceramic-based AM. Full article

This work presents the design and implementation of a mechanical test bench developed for the comparative evaluation of three configurations of a mechanical biomedical device: the reference version and two optimized alternatives aimed at improving long-term reliability and functional performance. The test bench performs mechanical fatigue testing under controlled and repeatable conditions, simulating the cyclic loads typical of real-world operation. A key innovation of this system is the integration of a non-invasive acoustic acquisition module, which continuously monitors the dynamic behavior of the device during testing. The analysis of acoustic signals allows for the early detection of wear, looseness, deformation, and the onset of structural defects, providing valuable insight into the device’s mechanical health without altering its configuration. This study also details the engineering design of the control system, emphasizing both hardware integration and software architecture supporting real-time signal processing. Experimental results demonstrate that acoustic analysis represents an effective non-destructive approach for evaluating the endurance and reliability of compact plastic biomedical devices. The proposed methodology contributes to more accurate service life estimation, supports product validation, and promotes continuous improvements in the safety and quality of mechanical systems used in biomedical applications. Full article