Search Results (80)

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

An important aspect of medical rehabilitation using exoskeletons is objective monitoring of the effectiveness of the exercise program. This control is most often manual and relies on the attention of a rehabilitation physician, but advanced rehabilitation systems also use computer vision technology. Topological skeletons generalize large areas of digital images, representing a virtual internal framework of the analyzed object. The patient and the exoskeleton are described either as a set of spatially disparate (but not explicitly related to either the patient or the exoskeleton) topological skeletons, or as branches of a single topological skeleton which does not allow for objective monitoring of joint displacements. A method to solve this problem for medical rehabilitation using an upper-limb exoskeleton is proposed. It includes the following stages: (I) identifying the exoskeleton, as well as upper and lower parts of the patient’s body; (II) independent construction of three topological skeletons (separately for the exoskeleton and for the upper and lower parts of the patient’s body); (III) their integration. This approach allows for accurate, real-time analysis of movements in the upper-limb joints and prompt notification to the rehabilitation physician of any significant deviations in the technique of performing prescribed exercises. Full article

There are significant differences in the deformation patterns of different parts of arch dams, and there is a common situation of periodic data loss. To accurately analyze the deformation behavior of arch dams, this paper proposes a safety warning and anomaly diagnosis method for arch dam deformation based on the separation of environmental variable effects in different partitions and a knowledge-driven approach. This method combines various techniques such as an optimized ISODATA clustering method, probabilistic principal component analysis (PPCA), square prediction error (SPE) norm control chart, and contribution chart. By defining data forms and rules, existing engineering specifications and experience are transformed into “knowledge” and applied to the operation and management of arch dams, achieving accurate monitoring of arch dam deformation status and timely diagnosis of outliers. Through monitoring data verification of horizontal displacement in a certain arch dam partition, the results show that this method can accurately identify deformation anomalies in the arch dam and effectively separate the influence of environmental variables and noise interference, providing strong support for the safe operation of the arch dam. Accurate deformation monitoring of arch dams is essential for ensuring structural safety and optimizing operational management. However, conventional early warning indicators and empirical models often fail to capture the spatial heterogeneity of deformation and the complex coupling between environmental variables and structural responses. To overcome these limitations, this study proposes a knowledge-driven safety early warning and anomaly diagnosis model for arch dam deformation, based on spatiotemporal clustering and partitioned environmental variable separation. The method integrates the optimized ISODATA clustering algorithm, probabilistic principal component analysis (PPCA), squared prediction error (SPE) control chart, and contribution chart to establish a comprehensive monitoring framework. The optimized ISODATA identifies deformation zones with similar mechanical behavior, PPCA separates environmental influences such as temperature and reservoir level from structural responses, and the SPE and contribution charts quantify abnormal variations and locate potential risk regions. Application of the proposed method to long-term deformation monitoring data demonstrates that the PPCA-based framework effectively separates environmental effects, improves the interpretability of zoned deformation characteristics, and enhances the accuracy and reliability of anomaly identification compared with conventional approaches. These findings indicate that the proposed knowledge-driven model provides a robust and interpretable framework for precise deformation safety evaluation of arch dams. Full article

This study investigates the development of axial force in micro anti-slide piles under soil movement during slope stabilization. Axial force arises from two primary mechanisms: axial soil displacement ($z_{\mathrm{s}}$) and pile kinematics. The former plays a dominant role, producing either tensile or compressive axial force depending on the direction of $z_{\mathrm{s}}$, while the kinematically induced component remains consistently tensile. A sliding angle of $\alpha =5°$ represents an approximate transition point where these two effects balance each other. Furthermore, the two mechanisms exhibit distinct mobilization behaviors: $z_{\mathrm{s}}$-induced axial force mobilizes earlier than both bending moment and shear force, whereas kinematically induced axial force mobilizes significantly later. The study reveals two distinct pile–soil interaction mechanisms depending on proximity to the slip surface: away from the slip surface, axial soil resistance is governed by rigid cross-section translation, whereas near the slip surface, rotation-dominated displacement accompanied by soil–pile separation introduces significant complexity in predicting both the magnitude and direction of axial friction. A hyperbolic formulation was adopted to model both the lateral soil resistance relative to lateral pile–soil displacement (p-y behavior) and the axial frictional resistance relative to axial pile–soil displacement (t-z behavior). Soil resistance equations were derived to explicitly incorporate the effects of cross-sectional rotation and pile–soil separation. A novel beam-spring finite element method (BSFEM) that incorporates both geometric and material nonlinearities of the pile behavior was developed, using a soil displacement-driven solution algorithm. Validation against both numerical simulations and field monitoring data from an engineering application demonstrates the model’s effectiveness in capturing the distribution and evolution of axial deformation and axial force in micropiles under varying soil movement conditions. Full article

To address the limitations of traditional elasticity theory in analyzing the dynamic response of soft coal under external impact, this study establishes a vibration control equation with an analytical solution based on Hamiltonian mechanics. Key control parameters within the equation were solved to determine the theoretical dominant vibration modes and natural frequencies of the weakest coal layer. Triangular and rectangular waves were transformed via FFT to analyze their harmonic components, and the superposition of the first four harmonics was selected as the input impact signal. The modal and natural frequency changes during the fragmentation of the central weak zone under external impact were simulated, and the dynamic displacement response was analyzed. The results indicate a strong response frequency range of 4.4–5.2 Hz, with the rectangular wave identified as the most effective response waveform. A similarity simulation platform was constructed, and experimental data showed that the velocity and displacement response trend of the coal mass aligned closely with theoretical predictions. Therefore, in actual underground operations, emphasis should be placed on monitoring low-frequency vibrations in mines, minimizing rectangular wave disturbances in the low-frequency range, and implementing pressure relief measures in high-risk zones to reduce the likelihood of coal and gas outbursts. By separately modeling high-risk zones and analyzing their dynamic response under external impact, this study explains the outburst mechanism of the weakest layer in soft coal from a dynamic perspective. Combining theoretical and experimental approaches, it provides a new theoretical basis for understanding and preventing coal and gas outbursts. Full article

Accurate ground deformation monitoring with interferometric synthetic aperture radar (InSAR) is often hindered by tropospheric delays caused by atmospheric pressure, temperature, and water vapor variations. While models such as ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis v5) provide first-order corrections, they often leave residual errors dominated by small-scale turbulent effects. To address this, we present a novel variational autoencoder with clustering (VAE-clustering) approach that performs unsupervised separation of atmospheric and deformation signals, followed by noise component removal via density-based clustering. The method is integrated into the MintPy pipeline for automated velocity and displacement time-series retrieval. We evaluate our approach on Sentinel-1 interferograms from three case studies: (1) land subsidence in Mashhad, Iran (2015–2022), (2) land subsidence in Tehran, Iran (2018–2021), and (3) postseismic deformation after the 2021 Acapulco earthquake. Across all cases, the method reduced the velocity standard deviation by approximately 70% compared to the ERA5 corrections, leading to more reliable displacement estimates. These results demonstrate that VAE-clustering can effectively mitigate residual tropospheric noise, improving the accuracy of large-scale InSAR time-series analyses for geohazard monitoring and related applications. Full article

With coal mines’ mining depth increasing, the stress environment in deep mining (including key factors such as high ground stress, strong disturbance, and complex geological structures, as well as stress redistribution after deformation of surrounding roadway rock) is complex, which leads to increasingly prominent deformation and failure problems for goaf-side roadways in thick coal seams. Surrounding rock deformation is difficult to control, and mine pressure behavior is violent, making traditional support technologies no longer able to meet the mining safety requirements of roadways in deep thick coal seams. Taking the 6311 working face of Tangkou Coal Mine as the engineering research background, this paper systematically summarizes the deformation and failure characteristics of goaf-side roadways in deep thick coal seams through field monitoring, borehole peeping, and other means, and conducts in-depth analysis of their failure mechanisms and influencing factors. Aiming at these problems, a synergistic support–unloading control method for goaf-side roadways is proposed, which integrates roof blasting pressure relief, coal pillar grouting reinforcement, and constant-resistance energy-absorbing anchor cable support. The effects of the unsupported scheme, original support scheme, and synergistic support–unloading control scheme are compared and analyzed through FLAC^3D numerical simulation. Further verification through field application shows that it has remarkable effects in controlling roadway convergence deformation, roof separation, and bolt (cable) stress. Specifically, compared with the original support schemes, the horizontal displacement on the coal pillar side is reduced by 89.5% compared with the original support scheme, and the horizontal displacement on the solid coal side is reduced by 79.3%; the vertical displacement on the coal pillar side is reduced by 45.8% and the vertical displacement on the solid coal side is reduced by 42.4%. Compared with the original support scheme, the maximum deformation of the roadway’s solid coal rib, roof, and coal pillar rib is reduced by 76%, 83%, and 88%, respectively, while the separation between the shallow and deep roof remains at a low level. The coal stress continues fluctuating stably during the monitoring period; the force on the bolts (cables) does not exceed the designed anchoring force, with sufficient bearing reserve space (47% remaining), and no breakage occurs, which fully proves the feasibility and effectiveness of the synergistic support–unloading control technology scheme. This technology realizes the effective control of on-site roadways and provides technical reference for the support engineering of coal mine goaf-side roadways under similar conditions. Full article

Suspension bridges, as a type of long-span bridge, usually have a larger longitudinal displacement at the end of the beam (LDBD). LDBD can be used to evaluate the safety of bridge components at the end of the beam. However, due to factors such as sensor failure and system maintenance, LDBD in the bridge health monitoring system is often missing. Therefore, this study reconstructs the missing part of LDBD based on the long short-term memory network (LSTM) and various data. Specifically, first, the monitoring data that may be related to LDBD in a suspension bridge is analyzed, and the temperature and beam end rotation angle data (RDBD) at representative locations are selected. Then, the temperature data at different places of the bridge are used as the input of the LSTM model to compare and analyze the prediction effect of LDBD. Next, RDBD is used as the input of the LSTM model to observe the prediction effect of LDBD. Finally, temperature and RDBD are used as the input of the LSTM model to observe whether the prediction effect of the LSTM model is improved. The results show that compared with other parts of the bridge, the prediction effect of the temperature inside the box girder in the main span as the model input is better; when RDBD is used as the input of the LSTM model, it is better than the prediction effect of temperature as the model input; temperature and RDBD have higher prediction accuracy when used as the input of the LSTM model together than when used separately as the input of the LSTM model. Full article

Early warning systems depend heavily on the accuracy of landslide displacement forecasts. This study focuses on the Bazimen landslide located in the Three Gorges Reservoir region and proposes a hybrid prediction approach combining support vector regression (SVR) and long short-term memory (LSTM) networks. These models are optimized via the K-Nearest Neighbor (KNN) algorithm. Initially, cumulative displacement data were separated into trend and cyclic elements using a smoothing approach. SVR and LSTM were then used to predict the components, and KNN was introduced to optimize input factors and classify the results, improving accuracy. The final KNN-optimized SVR-LSTM model effectively integrates static and dynamic features, addressing limitations of traditional models. The results show that LSTM performs better than SVR, with an RMSE and MAPE of 24.73 mm and 1.87% at monitoring point ZG111, compared to 30.71 mm and 2.15% for SVR. The sequential hybrid model based on KNN-optimized SVR and LSTM achieved the best performance, with an RMSE and MAPE of 23.11 mm and 1.68%, respectively. This integrated model, which combines multiple algorithms, offers improved prediction of landslide displacement and practical value for disaster forecasting in the Three Gorges area. Full article

This paper presents a formulation for the dynamic analysis of dam–reservoir–foundation systems, employing a coupled finite element model that integrates displacements and reservoir pressures. An innovative coupled approach, without separating the solid and fluid equations, is proposed to directly solve the single non-symmetrical governing equation for the whole system with non-proportional damping. For the modal analysis, a state–space method is adopted to solve the coupled eigenproblem, and complex eigenvalues and eigenvectors are computed, corresponding to non-stationary vibration modes. For the seismic analysis, a time-stepping method is applied to the coupled dynamic equation, and the stress–transfer method is introduced to simulate the nonlinear behavior, innovatively combining a constitutive joint model and a concrete damage model with softening and two independent scalar damage variables (tension and compression). This formulation is implemented in the computer program DamDySSA5.0, developed by the authors. To validate the formulation, this paper provides the experimental and numerical results in the case of the Cahora Bassa dam, instrumented in 2010 with a continuous vibration monitoring system designed by the authors. The good comparison achieved between the monitoring data and the dam–reservoir–foundation model shows that the formulation is suitable for simulating the modal response (natural frequencies and mode shapes) for different reservoir water levels and the seismic response under low-intensity earthquakes, using accelerograms measured at the dam base as input. Additionally, the dam’s nonlinear seismic response is simulated under an artificial accelerogram of increasing intensity, showing the structural effects due to vertical joint movements (release of arch tensions near the crest) and the concrete damage evolution. Full article

The acquisition of high-quality tree point cloud datasets facilitates research in various forestry fields, including tree species classification, diversity monitoring, and biomass estimation. However, due to limitations in sensor performance and occlusion between trees, tree point clouds acquired using LiDAR scanners often exhibit missing data. This not only degrades the quality of the point clouds, but also significantly reduces the number of usable samples. Therefore, this study proposed a tree point cloud enhancement system, which included the completion network and the sample augmentation network. The point cloud completion network utilized a transformer-based improved module to predict missing point clouds and combined up-sampling processing to progressively complete the point clouds from coarse to fine. This could improve the subsequent model decisions and performance through data balancing. On the other hand, the sample augmentation network, based on an adversarial learning strategy, separately constructed the generator and the classifier. By applying shape transformations, point displacements, and point drop to complete point cloud samples, the learnable parameters in the generator and the classifier were alternately optimized. This process enhanced both the quality and the quantity of the tree point cloud dataset. In addition, this study introduced a multi-head attention pooling layer, which further enhanced the joint network’s ability to learn and extract tree structural features. The experimental results showed that the completion network successfully restored missing tree point clouds of various types, achieving an average Chamfer Distance of 4.84 and an average F-score of 0.90. The experiments also demonstrated the effectiveness and robustness of the sample augmentation network, which improved classification accuracy by approximately 2.9% compared to the original dataset. Full article

Soil particle size distribution is a critical parameter in geotechnical and hydraulic engineering, particularly in applications such as dam seepage monitoring, building foundation assessments, and sediment transport. This study presents a novel algorithm for estimating soil particle sizes by analyzing their falling velocities in water, combining optical flow computation with chaotic motion analysis. To address the limitations of the classical Horn and Schunck method, particularly its sensitivity to large displacements and brightness variations, we introduced a coarse-to-fine warping strategy, an image decomposition step to separate dominant structures from fine textures, and the Charbonnier penalty function. The improved model achieved competitive accuracy compared to advanced optical flow algorithms. To manage turbulence and motion noise during particle settling, we incorporated a global flow analysis framework using streaklines, streak flow, and potential functions. This enabled the segmentation of laminar, turbulent, and rebound flow regions without requiring individual particle tracking. Soil particle sizes were then back-calculated from laminar flow velocities using Stokes’ Law. Experimental results confirmed the method’s accuracy for particle sizes ranging from 20 mm to 0.7 mm, significantly extending the measurable range of Sedimaging systems. The proposed approach shows strong potential for integration into dam-related particle monitoring applications and building-related monitoring systems requiring fine-resolution analysis. Full article

In this study, we analyzed an offshore wind turbine suction bucket foundation with supports, referred to as the supported suction bucket foundation. A numerical model of a typical 3MW wind turbine suction bucket foundation was established, and the accuracy of the numerical modeling method was validated. The daily wind and wave extreme data for 2022 monitored by a station in the eastern China Sea were converted into loads and applied to the conventional and supported suction bucket foundations. The bearing capacity of the two bucket foundations was, thus, compared and analyzed. The results show that the supported suction bucket foundation reduces the foundation displacement and amplitude. Also, the support structure effectively reduces the soil displacement around the bucket. The maximum displacement of the outer soil of the bucket decreases by about 89.8%, while that of the inner soil of the bucket decreases by about 70.7%. Increasing the supports can reduce the separation of the top lid from the inner soil below it. Further, the supported suction bucket foundation can effectively reduce the plastic strain of the soil around the bucket and reduce the plastic strain difference between the inner and outer soil of the bucket. Full article

This research focuses on monitoring and analyzing air pollutant emissions, mainly from passenger vehicles, at a busy urban intersection with 19 traffic lanes at the junction of Thivon Avenue and Iera Odos, located in the Egaleo municipality, an urban region of Athens, Greece. To collect data, a monitoring study was conducted specifically on the four central traffic streams of this specific intersection. On each segment of the road, a specific length was assigned through which vehicles pass at an average speed in order for their emissions to be estimated. For each vehicle, the engine type (gas or diesel) and engine displacement were taken into account to calculate the predicted mass of vehicle emissions. These measurements were conducted separately for each segment and recorded during three signal phases (from green to red) for two weekdays and one non-working day. This approach allows pollutant levels to be monitored at various hours and under various traffic conditions. The analysis revealed not only the overall quantity of emissions from vehicles but also their fluctuations throughout the day and traffic conditions, comparing them with the regulatory limits set by the EU. Significant findings regarding the impact of traffic on air quality are highlighted. Full article

The rapid development of coastal cities has intensified land resource constraints and is leading to an increasing number of foundation pit projects near existing operational tunnels. This necessitates careful consideration of coastal excavation impacts on adjacent tunnels. Taking a foundation pit project in Qingdao as a case study, this paper investigates tunnel deformation through statistical analysis, numerical simulation, and field monitoring. By adjusting numerical model parameters, the research examines the influence of horizontal clearance distances, existing structure burial depths, and different retaining structure configurations on tunnel deformation, providing guidance for deformation control. Key findings include the following: (1) Statistical analysis reveals that tunnels in silty clay strata experience more significant excavation-induced deformation compared to those in silt strata, with relative positional relationships between pits and tunnels playing a critical role. (2) Numerical and monitoring results demonstrate that pit excavation induces tunnel displacement towards the excavation zone. Maximum lateral displacement reached 3.57 mm (simulated) and 4.79 mm (measured), while maximum vertical displacement was 3.11 mm (simulated) and 3.85 mm (measured), all within safety thresholds. (3) Sensitivity analysis shows that shallower tunnels exhibit more pronounced deformations. Increasing horizontal separation distance from 10 m to 25 m reduces deformation by one-third. However, adjusting diaphragm wall thickness and retaining structure embedment depth proves limited in deformation control, necessitating reinforcement measures on the tunnel side. These findings provide valuable references for protecting coastal silty clay stratum tunnels. Full article