Search Results (751)

Coastal boulder deposits (CBDs) are important geomorphic indicators of extreme wave activity, yet integrated morphometric and hydrodynamic analyses remain limited along the Moroccan Atlantic coast. This study characterizes the morphology, spatial distribution, and transport thresholds of supratidal boulders at Oued Cherrat and Mansouria, and quantifies the wave energy required for their mobilization. Between 2021 and 2025, 85 boulders were surveyed, supported by lithological analyses, GPS mapping, and pre-/post-storm photographic documentation. At Oued Cherrat, boulders ranged from 0.01 to 3.56 m³ (≤7.84 t), with solitary blocks located 30–94 m inland and larger imbricated clasts up to 150.5 m. At Mansouria, dimensions reached 22 × 20 × 3.5 m (>2032 t), positioned 5–140 m from the shoreline. Storms in January and March 2025 displaced boulders up to 4.5 m at Oued Cherrat (e.g., 6.39 t) and up to 3 m at Mansouria (e.g., 21.42 t), with new blocks deposited and megaboulders showing slight in situ rotations. Hydrodynamic modelling estimated sliding thresholds of 1.1–4.0 m/s at Oued Cherrat and 2.7–11.0 m/s at Mansouria, while rolling thresholds reached 18.23 m/s. These values confirm the dependence of transport on boulder mass, imbrications, and topography. The findings demonstrate that extreme storms can rapidly reorganize multi-tonne CBDs, while the largest megaboulders require rare, exceptionally high-energy events. Full article

There has been significant interest in deploying unmanned aerial vehicles (UAVs) for their ability to perform precise and rapid remote mapping and inspection of critical environmental assets for structural health monitoring. This case study investigates the use of UAV-based LiDAR and photogrammetry at Melbourne Water’s Western Treatment Plant (WTP) to routinely monitor high-density polyethylene floating covers on anaerobic lagoons. The proposed approach integrates LiDAR and photogrammetry data to enhance the accuracy and efficiency of generating digital elevation models (DEMs) and orthomosaics by leveraging the strengths of both methods. Specifically, the photogrammetric images were orthorectified onto LiDAR-derived DEMs as the projection plane to construct the corresponding orthomosaic. This method captures precise elevation points directly from LiDAR, forming a robust foundation dataset for DEM construction. This streamlines the workflow without compromising detail, as it eliminates the need for time-intensive photogrammetry processes, such as dense cloud and depth map generation. This integration accelerates dataset production by up to four times compared to photogrammetry alone, while achieving centimetre-level accuracy. The LiDAR-derived DEM achieved higher elevation accuracy with a root mean square error (RMSE) of 56.1 mm, while the photogrammetry-derived DEM achieved higher in-plane accuracy with an RMSE of up to 35.4 mm. An analysis of cover deformation revealed that the floating cover had elevated rapidly within the first two years post-installation before showing lateral displacement around the sixth year, which was also evident from a significant increase in wrinkling. This approach delivers valuable insights into cover condition that, in turn, clarifies scum accumulation and movement, thereby enhancing structural integrity management and supporting environmental sustainability at WTP by safeguarding methane-rich biogas for renewable-energy generation and controlling odours. The findings support the ongoing collaborative industry research between Monash University and Melbourne Water, aimed at achieving comprehensive structural and prognostic health assessments of these high-value assets. Full article

During deep excavation, actual soil parameters undergo changes. To enhance the accuracy of soil parameter selection in finite element simulation and improve the precision of finite element analysis, an inversion method for soil parameters based on a PSO-SVM model is proposed. In this method, the particle swarm optimization (PSO) algorithm is utilized to optimize the penalty parameter C and kernel function parameter g of the support vector machine (SVM) model. The optimized PSO-SVM model is employed to establish a nonlinear mapping relationship between the horizontal displacements of retaining structures in deep excavations and soil parameters through orthogonal experimental design and finite element simulation analysis. Subsequently, soil parameters are inverted from monitoring data of horizontal displacements of retaining structures, and the reliability of the parameters is verified. The deformation of the retaining structures during subsequent cases is then predicted. The results demonstrate that the absolute error of the peak maximum horizontal displacements of the retaining structures after inversion is maintained within 1 mm. The maximum relative error is reduced from 18.96% before inversion to 7.63%, indicating that the inverted soil parameters for the deep excavation possess high accuracy. The precision of the finite element simulation for deep excavation is significantly improved, effectively reflecting the actual mechanical properties of the soil during the construction stage. The inverted parameters can be used for the prediction of subsequent retaining structure deformation. During subsequent construction conditions, the predicted maximum horizontal displacement (deformation) of the retaining structure at monitoring point CX1 is 15.66 mm, and that at monitoring point CX2 is predicted to be 14.22 mm. Neither value exceeds the project warning threshold of 30.00 mm. Full article

In this study, a nonlinear dynamic model is developed to examine the stability and vibration behavior of a self-balancing electric Segway operating over irregular stochastic terrains. The Segway is treated as a three-degrees-of-freedom cart–inverted pendulum system, incorporating elastic and damping effects at the wheel–ground interface. Road irregularities are generated in accordance with international standard using high-order filtered noise, allowing for representation of surface classes from smooth to highly degraded. The governing equations, formulated via Lagrange’s method, are transformed into a Lorenz-like state-space form for nonlinear analysis. Numerical simulations employ the fourth-order Runge–Kutta scheme to compute translational and angular responses under varying speeds and terrain conditions. Frequency-domain analysis using Fast Fourier Transform (FFT) identifies resonant excitation bands linked to road spectral content, while Kernel Density Estimation (KDE) maps the probability distribution of displacement states to distinguish stable from variable regimes. The Lyapunov stability assessment and bifurcation analysis reveal critical velocity thresholds and parameter regions marking transitions from stable operation to chaotic motion. The study quantifies the influence of the gravity–damping ratio, mass–damping coupling, control torque ratio, and vertical excitation on dynamic stability. The results provide a methodology for designing stability control systems that ensure safe and comfortable Segway operation across diverse terrains. Full article

This article aims to perform system identification of a nearly 30-year-old cable-stayed steel footbridge over the Wisłok River in Rzeszów (Poland). The design documentation of the bridge has been lost, and since its construction, the footbridge has been subject to renovations. The structure is highly susceptible to pedestrian traffic, and before any actions are taken to improve the comfort of use, it is necessary to create and validate a numerical model and assess the force distribution in the structure. Models are often built as mappings of an ideal structure. However, real structures are not ideal. The comparison of numerical and measured data can allow for an indication of potential damage areas. Two main purposes of the article have been formulated: (1)Development of a numerical model of an old footbridge, whose components have been degraded due to long-term use. Changes, compared to the ‘original’, focused on elongation of the cables due to rheology and a decrease in their tension. (2) Demonstrate the challenges in modeling and validating this type of bridge. In the article, the result of the numerical simulation (Finite Element Method and Ansys2024 R2 was applied, the verification was made in RFEM6) for models with different boundary conditions and varied pre-tension in cables was compared with the results of static and dynamic examination of a real object. The dynamic tests showed an uneven distribution of pre-tension in cables. The ratio of the first natural frequencies of inner cables on the north side is as high as 16%. The novelty demonstrated in the article is that static tests are insufficient for proper system identification; the same value of vertical displacement can be obtained for a selected static load, with varied tension in cables. Therefore, dynamic testing is essential. Full model updating requires a multicriteria approach, which will be made in the future. Full article

This article presents a novel approach to looking at steady-state thermoelastic boundary value problems in isotropic elastic plates with curvilinear holes using a complex variable approach and rational conformal mappings. The physical domain with a non-circular opening is mapped conformally to the unit disk. A thermoelastic potential combines the temperature distribution, which is determined by the Laplace equation with Neumann boundary conditions. Gaursat functions, which are shown as truncated power series, show the complicated stress and displacement fields. They are found by putting boundary constraints at certain collocation points. This procedure presents us with a linear system that can be solved using the least squares method. The method is applied in an annular shape that is exposed to a radial temperature gradient. This experiment shows how changes at the boundary affect the distribution of stress. According to numerical simulations, stress distributions are more uniform when boundaries are smoother, but stress concentrations increase with the size of geometric disturbances. The suggested approach remarkably captures the way geometry and thermal effects interact in two-dimensional thermoelasticity. It is a reliable tool for researching intricate, heated elastic domains. Full article

This article presents the ADAImpact tool, a QGIS plugin designed to assess the potential impacts of geohazards—such as landslides, subsidence, and sinkholes—using open-access surface displacement data from the European Ground Motion Service (EGMS), which is based on Sentinel-1 satellite observations. Created as part of the European RASTOOL project, ADAImpact integrates InSAR-derived ground movement data with exposure datasets (including population, infrastructure, and buildings) to support civil protection agencies in conducting risk assessments and planning emergency responses. The tool combines “Process Magnitude”, with “Exposure” metrics, quantifying the population and critical infrastructure affected, to generate potential impact maps for ground motion hazards. When applied to case studies along the Portugal–Spain border and the coastal region of Granada, Spain, ADAImpact successfully identified areas of high potential impact. These results underscore the tool’s utility in pre- and post-disaster assessment, highlighting its potential for scalability across Europe. Full article

This study aimed to evaluate how different insertion angles of titanium orthodontic miniscrews (30°, 45°, and 90°) influence stress distribution and displacement in surrounding alveolar bone using three-dimensional finite element analysis (FEA), with a focus on biomechanical outcomes at the titanium–bone interface. The 90° insertion angle generated the highest stress in cortical bone (58.2 MPa) but the lowest displacement (0.023 mm), while the 30° angle produced lower stress (36.4 MPa) but greater displacement (0.052 mm). The 45° angle represented a compromise, combining moderate stress (42.7 MPa) and displacement (0.035 mm). This simulation-based study was conducted between January and April 2025 at the Department of Orthodontics, Kocaeli Health and Technology University. A standardized 3D mandibular bone model (2 mm cortical and 13 mm cancellous layers) was constructed, and Ti-6Al-4V miniscrews (1.6 mm × 8 mm) were virtually inserted at 30°, 45°, and 90°. A horizontal orthodontic load of 2 N was applied, and von Mises stress and displacement values were calculated in ANSYS Workbench. Stress patterns were visualized using color-coded maps. The 90° insertion angle generated the highest von Mises stress in cortical bone (50.6 MPa), with a total maximum stress of 58.2 MPa, followed by 45° (42.7 MPa) and 30° (36.4 MPa) insertions (p < 0.001). Stress was predominantly concentrated at the cortical entry point, especially in the 90° model. In terms of displacement, the 90° group exhibited the lowest mean displacement (0.023 ± 0.002 mm), followed by 45° (0.035 ± 0.003 mm) and 30° (0.052 ± 0.004 mm), with statistically significant differences among all groups (p < 0.001). The 45° angle showed a balanced biomechanical profile, combining moderate stress and displacement values, as confirmed by post hoc analysis. From a biomimetics perspective, understanding how insertion angle affects bone response provides insights for designing bio-inspired anchorage systems. By simulating natural stress dissipation, this study demonstrates that insertion angle strongly modulates miniscrew performance. Vertical placement (90°) ensures rigidity but concentrates cortical stress, whereas oblique placement, particularly at 45°, offers a balanced compromise with adequate stability and reduced stress. These results emphasize that beyond material properties, surgical parameters such as insertion angle are critical for clinical success. Full article

In response to the growing demand for automation in aerospace harness manufacturing, this study proposes a digital twin-based intelligent monitoring system for robotic wiring operations. The system integrates a seven-degree-of-freedom robotic platform with an adaptive servo gripper and employs a five-dimensional digital twin framework to synchronize physical and virtual entities. Key innovations include a coordinated motion model for minimizing joint displacement, a particle-swarm-optimized backpropagation neural network (PSO-BPNN) for adaptive gripping based on wire characteristics, and a virtual–physical closed-loop interaction strategy covering the entire wiring process. Methodologically, the system enables motion planning, quality prediction, and remote monitoring through Unity3D visualization, SQL-driven data processing, and real-time mapping. The experimental results demonstrate that the system can stably and efficiently complete complex wiring tasks with 1:1 trajectory reproduction. Moreover, the PSO-BPNN model significantly reduces prediction error compared to standard BPNN methods. The results confirm the system’s capability to ensure precise wire placement, enhance operational efficiency, and reduce error risks. This work offers a practical and intelligent solution for aerospace harness production and shows strong potential for extension to multi-robot collaboration and full production line scheduling. Full article

Characterizingthe spatial variability of agricultural data is a fundamental step in precision agriculture, especially in soil management and the creation of differentiated management units for increasing productivity. Modeling the spatial dependence structure using geostatistical methods is of great importance for efficiency, estimating the parameters that define this structure, and performing kriging-based interpolation. This work presents diagnostic techniques for global and local influence and generalized leverage using the displacement of the conditional expectation of the logarithm of the joint-likelihood, called the Q-function. This method is used to identify the presence of influential observations that can interfere with parameter estimations, geostatistics model selection, map construction, and spatial variability. To study spatially correlated data, we used reparameterized t-Student distribution linear spatial modeling. This distribution has been used as an alternative to the normal distribution when data have outliers, and it has the same form of covariance matrix as the normal distribution, which enables a direct comparison between them. The methodology is illustrated using one real data set, and the results showed that the modeling was more robust in the presence of influential observations. The study of these observations is indispensable for decision-making in precision agriculture. Full article

Rainfall is a key triggering factor for numerous geotechnical hazards. Hence, it is necessary to investigate the degradation characteristics of rock–soil strength under different water contents. The existing macro–mesoscopic analysis methods for rock–soil strength degradation neglect the intrinsic connection between water content variations caused by external rainfall and mesoscopic mechanical mechanisms. In addition, there is a lack of discrete element method (DEM) mesoscopic parameter calibration methods for rock–soil strength under the influence of external environmental factors. Hence, this study aims to perform a macro–mesoscopic analysis and develop a parameter calibration model for the degradation of rock–soil strength under different water contents. First, the mesoscopic mechanical characteristics under different water contents are investigated by analyzing particle displacement, the bond failure rate, and the anisotropy coefficient. Interrelationships among shear strength, water content, and mesoscopic parameters are qualitatively analyzed, which indicated a macro–mesoscopic synergistic mechanism. A macro–meso-environment data set is constructed. Key mesoscopic parameters are determined using Pearson correlation (Pearson) and mutual information (MI) methods. Then, the mapping relationships are established based on ordinary least squares. The model accuracy is verified by comparing the calibrated simulation results with direct shear test results. The results show that the shear strength increases with vertical pressure under a constant water content. However, as the water content varies, the strength initially increases and then decreases. The average displacement of central particles and bond failure rate both decrease initially and then increase with rising water content, while the anisotropy coefficients show the opposite trend. Normal bond strength, tangential bond strength, and friction coefficient are determined as the key parameters. The goodness-of-fit R² of the parameter calibration model exceeds 0.92. Among 45 validation working conditions, only two are found to have errors of 12.4% and 13.6%, and the remainder have errors below 5%. Full article

Accurate identification of mesoscopic parameters is critical for understanding the cracking and failure mechanisms of hydraulic concrete and for improving the reliability of numerical simulations. Traditional trial-and-error methods for parameter calibration are inefficient and often lack robustness. To address this issue, this study proposes a novel inversion method combining Support Vector Regression (SVR) with a Hybrid Grey Wolf Optimization (HGWO) algorithm. First, a mesoscopic simulation dataset of three-point bending (TPB) tests was constructed using 3D numerical models with varying mesoscopic parameters. Then, an SVR-based surrogate model was trained to learn the nonlinear mapping between mesoscopic parameters and load–CMOD (Crack Mouth Opening Displacement) curves. The HGWO algorithm was employed to optimize the SVR hyperparameters (penalty factor C and kernel coefficient g) and subsequently used to invert the mesoscopic parameters by minimizing the discrepancy between experimental and predicted CMOD values. The proposed method was validated through inversion of the mortar parameters of a tertiary hydraulic concrete beam. The results demonstrate that the HGWO-SVR model achieves high prediction accuracy (R² = 0.944, MAE = 1.220, MAPE = 0.041) and significantly improves computational efficiency compared to traditional methods. The simulation based on the inversed parameters yields load–CMOD curves that agree well with experimental results. This approach provides a promising and efficient tool for mesoscopic parameter identification of heterogeneous materials in hydraulic structures. Full article

Loading of the Earth’s crust due to variations in global atmospheric pressure can displace the position of geodetic stations. However, the station displacements induced by the diurnal and semidiurnal atmospheric tides (S₁-S₂) are often neglected during Global Positioning System (GPS) processing. We first studied the magnitudes of S₁-S₂ deformation in the Earth’s center of mass (CM) frame and compared the global S₁-S₂ grid models provided by the Global Geophysical Fluid Center (GGFC) and the Vienna Mapping Function (VMF) data server. The magnitude of S₁-S₂ tidal displacement can reach 1.5 mm in the Up component at low latitudes, approximately three times that of the horizontal components. The most significant difference between the GGFC and VMF grid models lies in the phase of S₂ in the horizontal components, with phase discrepancies of up to 180° observed at some stations. To investigate the effects of S₁-S₂ corrections on GPS coordinates, we then processed GPS data from 108 International GNSS Service (IGS) stations using the precise point positioning (PPP) method in two processing strategies, with and without the S₁-S₂ correction. We observed that the effects of S₁-S₂ on daily GPS coordinates are generally at the sub-millimeter level, with maximum root mean square (RMS) coordinate differences of 0.18, 0.08, and 0.51 mm in the East, North, and Up components, respectively. We confirmed that part of the GPS draconitic periodic signals was induced by unmodeled S₁-S₂ loading deformation, with the amplitudes of the first two draconitic harmonics induced by atmospheric tide loading reaching 0.2 mm in the Up component. Moreover, we recommend using the GGFC grid model for S₁-S₂ corrections in GPS data processing, as it reduced the weighted RMS of coordinate residuals for 45.37%, 46.30%, and 53.70% of stations in the East, North, and Up components, respectively, compared with 39.81%, 44.44%, and 50.00% for the VMF grid model. The effects of S₁-S₂ on linear velocities are very limited and remain within the Global Geodetic Observing System (GGOS) requirements for the future terrestrial reference frame at millimeter level. Full article

Internally displaced people (IDPs) are individuals forced to leave their homes due to conflicts or disasters without crossing international borders. Since 2008, weather-related extreme events—primarily storms and floods—have displaced more than 20 million people annually. With global temperatures rising and extreme weather intensifying, the number of IDPs is projected to increase in the coming decades. In the Philippines, resettlement has emerged as a key climate adaptation strategy, with IDP camps established to reduce risks in highly vulnerable areas. Yet, it remains unclear whether these camps are actually located in regions of lower climate vulnerability. This study aims to examine the climate vulnerability of 17 IDP camps by considering physical and infrastructural dimensions to assess whether they are located in safer areas, and to suggest the development of urban forms that can improve community resilience and the living conditions of their populations. Results show significant variation in climate vulnerability, with Villa Diana scoring the lowest and Villa Sofia the highest. Using emergency response facilities as a proxy for social capital, we identified drivers of vulnerability: Villa Sofia faces heightened risks due to population density, flood exposure, and limited emergency facilities, while Villa Diana benefits from greater emergency capacity and abundant vegetation that reduces risk. Our findings provide a systematic framework for assessing climate vulnerability among IDPs and highlight the critical role of social capital in mitigating climate impacts for displaced populations in the Global South, where climate risk mapping and reliable data remain limited. Full article

Contact-force perception is a critical component of safe robotic grasping. With the rapid advances in embodied intelligence technology, humanoid robots have enhanced their multimodal perception capabilities. Conventional force sensors face limitations, such as complex spatial arrangements, installation challenges at multiple nodes, and potential interference with robotic flexibility. Consequently, these conventional sensors are unsuitable for biomimetic robot requirements in object perception, natural interaction, and agile movement. Therefore, this study proposes a sensorless external force detection method that integrates SuperPoint-Scale Invariant Feature Transform (SIFT) feature extraction with finite element analysis to address force perception challenges. A visual analysis method based on the SuperPoint-SIFT feature fusion algorithm was implemented to reconstruct a three-dimensional displacement field of the target object. Subsequently, the displacement field was mapped to the contact force distribution using finite element modeling. Experimental results demonstrate a mean force estimation error of 7.60% (isotropic) and 8.15% (anisotropic), with RMSE < 8%, validated by flexible pressure sensors. To enhance the model’s reliability, a dual-channel video comparison framework was developed. By analyzing the consistency of the deformation patterns and mechanical responses between the actual compression and finite element simulation video keyframes, the proposed approach provides a novel solution for real-time force perception in robotic interactions. The proposed solution is suitable for applications such as precision assembly and medical robotics, where sensorless force feedback is crucial. Full article