Search Results (567)

This work presents a simple yet effective anti-interference demultiplexing method for electromagnetic Bessel beams carrying orbital angular momentum (OAM), based on smoothed dynamic mode decomposition (smoothed DMD). The method combines conventional dynamic mode decomposition (DMD) with a moving average pre-processing step to enhance its noise resilience. By modeling the azimuthally sampled field as a spatial–temporal signal, smoothed DMD enables accurate extraction of OAM topological charges even under low signal-to-noise ratio (SNR) conditions. Numerical results demonstrate its superior anti-interference performance compared to standard DMD. Moreover, the proposed approach is applicable to scenarios with partial aperture detection and does not rely on the orthogonality of OAM modes, making it particularly suitable for real-world, imperfect conditions. This method offers a robust solution for OAM beam analysis in next-generation wireless communication and sensing applications. Full article

This paper theoretically and numerically investigates temperature-dependent band structures in elastic metamaterial lattices using a plane wave expansion method incorporating thermal effects. We first analyze a one-dimensional (1D) elastic metamaterials beam, demonstrating that band frequencies decrease with rising temperature and increase with cooling. Then, the method is extended to square and rectangular 2D lattices, where temperature variations show remarkable influence on individual bands; while all bands shift to higher frequencies monotonically with cooling, their rates of change diminish asymptotically as they approach characteristic limiting values. Band structure predictions are validated against frequency response simulations of finite-structure. We further characterize temperature dependence of bands and bandgap widths, and quantify thermal sensitivity for the first four bands. These findings establish passive, robust thermal tuning strategies for ultralow frequency vibration suppression, offering new design routes for space-deployed lattice structures. Full article

To investigate the influence mechanism of hanger damage and arch-beam combined parameters on the failure behavior of tied-arch bridges, this study employs an advanced damage failure model within the LS-DYNA. A comprehensive simulation of the entire failure process was conducted, considering the coupled effects of hanger damage parameters and structural parameters of the arch-beam system, using a tied-arch bridge as the engineering case. The primary innovation of this study lies in overcoming the limitations of previous research, which has largely been confined to single hanger failure or static parameter analysis, by achieving, for the first time, dynamic tracking and quantitative identification of structural failure paths under the coupled influence of multiple parameters. The results demonstrate that both the severity and spatial distribution pattern of hanger damage significantly influence the structural failure mechanism. When damage is either uniformly distributed across the bridge or relatively concentrated—particularly when long hangers experience severe degradation—the structure becomes susceptible to cascading stress redistribution, substantially increasing the risk of global progressive collapse. This finding provides a theoretical foundation for developing risk-informed maintenance and repair strategies for hangers. It is therefore recommended that practical maintenance efforts prioritize monitoring the condition of long hangers and regions with concentrated damage. Furthermore, variations in arch-beam combined parameters are shown to have a significant effect on the structure’s collapse resistance. For the case bridge studied herein, the original design parameters achieve an optimal balance between anti-collapse performance and economic efficiency, underscoring the importance of rational parameter selection in enhancing system robustness. This work offers both theoretical insights and numerical tools for evaluating and optimizing the collapse-resistant performance of under-deck tied-arch bridges, contributing meaningful engineering value toward improving the safety and durability of similar structures. Full article

Terrestrial laser scanners (TLS) are widely used for deformation monitoring due to their ability to rapidly generate 3D point clouds. However, high-precision deliverables are increasingly required in TLS-based remote sensing applications to distinguish between measurement accuracies and actual geometric displacements. This study addresses the impact of atmospheric refraction, a primary source of systematic error in long-range terrestrial laser scanning, which causes laser beams to deviate from their theoretical path and intersect different object points on the target surface. A comprehensive study of two physical refractive index models (Ciddor and Closed Formula) is presented here, along with further developments on 3D spatial gradients of the refractive index. Field experiments were conducted using two long-range terrestrial laser scanners (Leica ScanStation P50 (Leica Geosystems, Heerbrugg, Switzerland) and Maptek I-Site 8820 (Maptek, Adelaide, Australia)) with reference back to a control network at two monitoring sites: a mine site for long-range measurements and a dam site for vertical angle measurements. The results demonstrate that, while conventional physical atmospheric models provide moderate improvement in accuracy, typically at the centimeter- or millimeter-level, the proposed advanced physical model—incorporating refractive index gradients—and the hybrid physical model—combining validated field results from the advanced model with a neural network algorithm—consistently achieve reliable millimeter-level accuracy in 3D point coordinates, by explicitly accounting for refractive index variations along the laser path. The robustness of these findings was further confirmed across different scanners and scanning environments. Full article

This study presents a strut-and-tie modeling (STM) framework for reinforced concrete (RC) deep beams strengthened with intraply hybrid composites (IRCs), integrating comprehensive experimental data from beams with three different span lengths (1.0 m, 1.5 m, and 2.0 m). Although the use of fiber-reinforced polymers (FRPs) for shear strengthening of RC members is well established, limited attention has been given to the development of STM formulations specifically adapted for hybrid composite systems. In this research, three distinct IRC configurations—Aramid–Carbon (AC), Glass–Aramid (GA), and Carbon–Glass (CG)—were applied as U-shaped jackets to RC beams without internal transverse reinforcement and tested under four-point bending. All experimental data were derived from the authors’ previous studies, ensuring methodological consistency and providing a robust empirical basis for model calibration. The proposed modified STM incorporates both the axial stiffness and effective strain capacity of IRCs into the tension tie formulation, while also accounting for the enhanced diagonal strut performance arising from composite confinement effects. Parametric evaluations were conducted to investigate the influence of the span-to-depth ratio (a/d), composite configuration, and failure mode on the internal force distribution and STM topology. Comparisons between the STM-predicted shear capacities and experimental results revealed excellent correlation, particularly for deep beams (a/d = 1.0), where IRCs substantially contributed to the shear transfer mechanism through active tensile engagement and confinement. To the best of the authors’ knowledge, this is the first study to formulate and validate a comprehensive STM specifically designed for RC deep beams strengthened with IRCs. The proposed approach provides a unified analytical framework for predicting shear strength and optimizing the design of composite-strengthened RC structures. Full article

In saline soil and alpine regions of northwest China, fiber-reinforced recycled aggregate concrete (FR-RAC) beams are subjected to coupled degradation from a chloride–sulfate composite salt attack and freeze–thaw cycling. Existing studies predominantly focus on natural aggregate concrete in freshwater environments or single-salt solutions, with limited documentation on the shear performance of FR-RAC beams after freeze–thaw exposure in chloride–sulfate composite salt solutions. To investigate the durability degradation patterns of FR-RAC beams in Xinjiang’s saline soil regions, two exposure environments (pure water and 5% NaCl + 2.0% Na₂SO₄ composite salt solution) were established. Shear performance tests were conducted on nine groups of FR-RAC beams after 0–175 freeze–thaw cycles, with measurements focusing on failure modes, cracking loads, and ultimate shear capacities. The results revealed that under composite salt freeze–thaw conditions: after 100 cycles, the cracking load and shear capacity of tested beams decreased by 39.8% and 22.2%, respectively, compared to unfrozen specimens representing reductions 29.6% and 82.0% greater than those in freshwater environments; at 175 cycles, cumulative damage intensified, with total reductions reaching 56.8% (cracking load) and 36.1% (shear capacity). A shear capacity degradation prediction model for FR-RAC beams under composite salt freeze–thaw coupling was developed, accounting for concrete strength attenuation and interfacial bond degradation. Model validation demonstrated excellent agreement between predicted and experimental values, confirming its robust applicability. Full article

This paper presents Rain-Cloud Condensation Optimizer (RCCO), a nature-inspired metaheuristic that maps cloud microphysics to population-based search. Candidate solutions (“droplets”) evolve under a dual-attractor dynamic toward both a global leader and a rank-weighted cloud core, with time-decaying coefficients that progressively shift emphasis from exploration to exploitation. Diversity is preserved via domain-aware coalescence and opposition-based mirroring sampled within the coordinate-wise band defined by two parents. Rare heavy-tailed “turbulence gusts” (Cauchy perturbations) enable long jumps, while a wrap-and-reflect scheme enforces feasibility near the bounds. A sine-map initializer improves early coverage with negligible overhead. RCCO exposes a small hyperparameter set, and its per-iteration time and memory scale linearly with population size and problem dimension. RCOO has been compared with 21 state-of-the-art optimizers, over the CEC 2022 benchmark suite, where it achieves competitive to superior accuracy and stability, and achieves the top results over eight functions, including in high-dimensional regimes. We further demonstrate constrained, real-world effectiveness on five structural engineering problems—cantilever stepped beam, pressure vessel, planetary gear train, ten-bar planar truss, and three-bar truss. These results suggest that a hydrology-inspired search framework, coupled with simple state-dependent schedules, yields a robust, low-tuning optimizer for black-box, nonconvex problems. Full article

Prior studies on indoor LiDAR point-cloud semantic segmentation consistently report that sampling density strongly affects segmentation accuracy as well as runtime and memory, establishing an accuracy–efficiency trade-off. Nevertheless, in practice, the density is often chosen heuristically and reported under heterogeneous protocols, which limits quantitative guidance. We present a unified evaluation framework that treats density as the sole independent variable. To control architectural variability, three representative backbones—PointNet, PointNet++, and DGCNN—are each augmented with an identical Point Transformer module, yielding PointNet-Trans, PointNet++-Trans, and DGCNN-Trans trained and tested under one standardized protocol. The framework couples isotropic voxel-guided uniform down-sampling with a decision rule integrating three signals: (i) accuracy sufficiency, (ii) the onset of diminishing efficiency, and (iii) the knee of the accuracy–density curve. Experiments on scan-derived indoor point clouds (with BIM-derived counterparts for contrast) quantify the accuracy–runtime trade-off and identify an engineering-feasible operating band of 1600–2900 points/m², with a robust setting near 2400 points/m². Planar components saturate at moderate densities, whereas beams are more sensitive to down-sampling. By isolating density effects and enforcing one protocol, the study provides reproducible, model-agnostic guidance for scan planning and compute budgeting in indoor mapping and Scan-to-BIM workflows. Full article

With the rapid proliferation of unmanned aerial vehicles (UAVs) and the associated rise in security concerns, there is a growing demand for robust detection and identification systems capable of operating reliably in complex electromagnetic environments. To address this challenge, this paper proposes a deep learning-based passive UAV detection and identification system leveraging radio frequency (RF) spectrograms. The system employs a high-resolution RF front-end comprising a multi-beam directional antenna and a wideband spectrum analyzer to scan the target airspace and capture UAV signals with enhanced spatial and spectral granularity. A YOLO-based detection module is then used to extract frequency hopping signal (FHS) regions from the spectrogram, which are subsequently classified by a convolutional neural network (CNN) to identify specific UAV models. Extensive measurements are carried out in both line-of-sight (LoS) and non-line-of-sight (NLoS) urban environments. The proposed system achieves over 96% accuracy in both detection and identification under LoS conditions. In NLoS conditions, it improves the identification accuracy by more than 15% compared with conventional full-spectrum CNN-based methods. These results validate the system’s robustness, real-time responsiveness, and strong practical applicability. Full article

Accurate estimation of the forest canopy height is crucial in monitoring the global carbon cycle and evaluating progress toward carbon neutrality goals. The Global Ecosystem Dynamics Investigation (GEDI) mission provides an important data source for canopy height estimation at a global scale. However, the non-zero half-width of the transmitted laser pulses (NHWTLP) and the influence of terrain slope can cause waveform broadening and overlap between canopy returns and ground returns in GEDI waveforms, thereby reducing the estimation accuracy. To address these limitations, we propose a canopy height retrieval method that combines the deconvolution of GEDI’s transmitted waveforms with terrain slope constraints on the ground response function. The method consists of two main components. The first is performing deconvolution on GEDI’s effective return waveforms using their corresponding transmitted waveforms to obtain the true ground response function within each GEDI footprint, thereby mitigating waveform broadening and overlap induced by NHWTLP. This process includes constructing a convolution convergence function for GEDI waveforms, denoising GEDI waveform data, transforming one-dimensional ground response functions into two dimensions, and applying amplitude difference regularization between the convolved and observed waveforms. The second is incorporating terrain slope parameters derived from a digital terrain model (DTM) as constraints in the canopy height estimation model to alleviate waveform broadening and overlap in ground response functions caused by topographic effects. The proposed approach enhances the precision of forest canopy height estimation from GEDI data, particularly in areas with complex terrain. The results demonstrate that, under various conditions—including GEDI full-power beams and coverage beams, different terrain slopes, varying canopy closures, and multiple study areas—the retrieved height (rh) model constructed from ground response functions derived via the inverse deconvolution of the transmitted waveforms (IDTW) outperforms the RH (the official height from GEDI L2A) model constructed using RH parameters from GEDI L2A data files in forest canopy height estimation. Specifically, without incorporating terrain slope, the rh model for canopy height estimation using full-power beams achieved a coefficient of determination (R²) of 0.58 and a root mean square error (RMSE) of 5.23 m, compared to the RH model, which had an R² of 0.58 and an RMSE of 5.54 m. After incorporating terrain slope, the rh_g model for full-power beams in canopy height estimation yielded an R² of 0.61 and an RMSE of 5.21 m, while the RH_g model attained an R² of 0.60 and an RMSE of 5.45 m. These findings indicate that the proposed method effectively mitigates waveform broadening and overlap in GEDI waveforms, thereby enhancing the precision of forest canopy height estimation, particularly in areas with complex terrain. This approach provides robust technical support for global-scale forest resource assessment and contributes to the accurate monitoring of carbon dynamics. Full article

A novel closed loop visual positioning system, termed YOLO–LaserGalvo (YLGS), is proposed for precise localization of welding torch tips in industrial welding automation. The proposed system integrates a monocular camera, an infrared laser distance sensor with a galvanometer scanner, and a customized deep learning detector based on an improved YOLOv11 model. In operation, the vision subsystem first detects the approximate image location of the torch tip using the YOLOv11-based model. Guided by this detection, the galvanometer steers the IR laser beam to that point and measures the distance to the torch tip. The distance feedback is then fused with the vision coordinates to compute the precise 3D position of the torch tip in real-time. Under complex illumination, the proposed YLGS system exhibits superior robustness compared with color-marker and ArUco baselines. Experimental evaluation shows that the system outperforms traditional color-marker and ArUco-based methods in terms of accuracy, robustness, and processing speed. This marker-free method provides high-precision torch positioning without requiring structured lighting or artificial markers. Its pedagogical implications in engineering education are also discussed. Potential future work includes extending the method to full 6-DOF pose estimation and integrating additional sensors for enhanced performance. Full article

A novel tunable E-band (78–82 GHz) Reconfigurable Electro-Mechanical Reflectarray (REMR) is demonstrated for beam focusing and steering. This design is based on our previous study, which achieved a 350° phase dynamic range in simulations, and is experimentally validated here. The results confirm precise beam focusing and ±3° beam steering, showing excellent agreement with simulations. To further enhance performance, an innovative dual-patch unit cell with a flat ground plane is introduced, enabling nearly 360° phase coverage and extended beam steering up to ±6°. The simplified architecture reduces fabrication complexity and cost while providing a compact, robust, and power-efficient solution for E-band and millimeter-wave communication systems. Full article

This study investigates the shear behavior of concrete elements reinforced with both traditional steel reinforcement and macro-synthetic fibers, with an emphasis on evaluating the predictive capabilities of current shear design provisions. A review of available experimental data, involving 52 beams and 8 panel specimens, revealed limitations in both quantity and consistency, hindering the formulation of robust design recommendations. To address this, an extensive parametric numerical study was conducted using the VecTor2 nonlinear finite element program, incorporating a recently developed modeling approach for PFRC shear response. A total of 288 simulations were carried out to explore the influence of fiber content, transverse reinforcement ratio, and concrete compressive strength, particularly in ranges not previously captured by experimental programs. The performance of existing design codes, including ACI, CSA, EC2, AASHTO, and the Fib Model Code, was assessed against both experimental data and the enriched parametric dataset. The Fib Model Code demonstrated the most reliable and consistent predictions, maintaining close alignment with reference strengths across all fiber contents, reinforcement ratios, and concrete strengths. AASHTO provisions performed moderately well, showing generally conservative and stable predictions, though some underestimation occurred for beams with higher shear reinforcement. In contrast, ACI and CSA models were consistently conservative, especially at higher concrete strengths, potentially leading to uneconomical designs. EC2 models exhibited the highest variability and least reliability, particularly in the presence of fibers, indicating limited applicability without modification. The results highlight that most conventional codes do not fully account for the synergistic action between fibers and transverse steel reinforcement, and that their reliability deteriorates for high-strength PFRC. These findings have practical implications for the design of PFRC elements, suggesting that the Fib Model Code may be the most suitable for current applications, whereas other provisions may require recalibration or modification. Future research should focus on expanding experimental datasets and developing unified design models that explicitly consider fiber–steel interactions, concrete strength, and fiber distribution. Full article

This paper presents a comprehensive study on automated defect detection in complex structures using phased array ultrasonic testing data, focusing on both traditional signal processing and advanced deep learning methods. As a non-AI baseline, the well-known signal-to-noise ratio algorithm was improved by introducing automatic depth gate calculation using derivative analysis and eliminated the need for manual parameter tuning. Even though this method demonstrates robust flaw indication, it faces difficulties for automatic defect detection in highly noisy data or in cases with large pore zones. Considering this, multiple DL architectures—including fully connected networks, convolutional neural networks, and a novel Convolutional Attention Temporal Transformer for Sequences—are developed and trained on diverse datasets comprising simulated CIVA data and real-world data files from welded and composite specimens. Experimental results show that while the FCN architecture is limited in its ability to model dependencies, the CNN achieves a strong performance with a test accuracy of 94.9%, effectively capturing local features from PAUT signals. The CATT-S model, which integrates a convolutional feature extractor with a self-attention mechanism, consistently outperforms the other baselines by effectively modeling both fine-grained signal morphology and long-range inter-beam dependencies. Achieving a remarkable accuracy of 99.4% and a strong F1-score of 0.905 on experimental data, this integrated approach demonstrates significant practical potential for improving the reliability and efficiency of NDT in complex, heterogeneous materials. Full article

Reverse arch beam string-inclined column structures have been applied in large-scale event venues due to their unique load-bearing characteristics. However, ensuring their resistance to progressive collapse remains a critical challenge. To investigate the structural robustness of reverse arch beam string-inclined column structure in practical engineering applications, a simplified finite element model is developed herein using ANSYS APDL. The natural frequencies of the actual engineering structure are measured through the hammering method to validate the accuracy of the simulation model. Based on the component removal method, different structural components are removed and finite element analysis is carried out. The dynamic response of the overall structure and the importance coefficients of individual components after removal are examined. The results demonstrate good agreement between the natural frequencies measured by the impact hammer test and those predicted by the finite element simulations, with the difference being only 1.67%. It is found that upper beam failure is fatal to this structure; the outer inclined columns significantly affect the robustness of the structure, while the failure of a single strut has a negligible impact. According to the component division, the importance of the overall robustness of the structure is in the following order: upper beam > column end > column base > strut. The maximum stress is mostly located in beam 7, beam 8, beam 28, and beam 107, which needs to be focused on. Full article