Search Results (11,540)

Ductility, as a fundamental mechanical property, allows structures to undergo inelastic deformations and dissipate seismic energy while maintaining their load-carrying capacity without substantial strength degradation. Thus, the estimation of structural ductility demand has consistently constituted an essential topic of research interest in earthquake engineering. In this study, an iterative procedure for estimating the ductility demand of elastoplastic single-degree-of-freedom (SDOF) systems through dissipated energy is introduced. The proposed procedure helps the determination of ductility demand by use of only elastic response spectra. It initially estimates the hysteretic energy as a proportion of the total input energy. Then, ductility demand is estimated with the help of a developed equation by performing regression analyses based on the nonlinear time history analyses results of elastoplastic single-degree-of-freedom (SDOF) systems with a certain strength. Time history analyses were carried out by using an extensive earthquake ground motion database, which includes a total of 268 far-field records, two horizontal components from 134 recording stations located on firm soil sites. Full article

Achieving robust visual detection under challenging conditions such as poor illumination and target deformation remains a critical challenge for computer vision systems. Although the YOLO series of object detection algorithms excel in speed and accuracy, their performance significantly degrades under non-ideal lighting conditions. To address this issue, we propose a Hybrid Dynamic Perception Network (HDPNet), a framework specifically designed for high-precision, real-time object detection in harsh environments. HDPNet integrates three core modules into the YOLOv8n architecture to form a hybrid structure. The Dynamic Illumination-aware Module (DIM) adaptively enhances features under varying illumination through global encoding and a dual-attention mechanism, the Interactive Attention Fusion Network (IAFN) optimizes cross-modal features using a lightweight Transformer-CNN interactive architecture, and the Multi-branch Decomposition Network (MDN) captures multi-scale deformation features by combining deformable convolution and sparse Transformer. Experimental results on the self-built low-light industrial express package dataset named njpackage show that our method achieves an mAP@0.5 of 86.6%, which is 4.4% higher than the baseline YOLOv8n model, while maintaining real-time inference speed of ≥45 FPS. The proposed HDPNet not only provides an effective solution for logistics automation but also offers a robust and versatile hybrid technical framework adaptable to other vision tasks facing similar challenges. Full article

Gunshot injuries are challenging conditions because of the unique characteristics of the wounding agents producing soft tissue damage that may be compounded by the formation of an expanding temporary cavity (cavitation). Variations in ballistic performance leading to higher energy transfer by the projectile, including bullet tumbling, deformation, and fragmentation, cause increased soft tissue injury and may also lead to more extensive bone comminution compromising local blood supply. Once life-threatening injuries have been excluded or properly addressed, the emergency management of localized trauma from bullets and shotgun pellets may be complicated due to progressive tissue necrosis within the zone of injury. Additionally, the risk of infection should be tackled, especially in high energy bone injuries. War experience suggests a baseline separation between wounds with limited tissue destruction which can routinely be managed as simple penetrating injuries and those resulting from high energy transfer to the tissues involving a substantial amount of necrotic elements surrounding the wound channel which call for a more aggressive surgical approach. A further justification for such a distinction is the need for antibiotic therapy, which varies according to most studies depending on the wounding mechanism, the nature of the wound, and the extent of tissue injury. The emergency physician should also be aware of the possibility of “bizarre” bullet paths resulting in occult injuries of important anatomic structures. Full article

Mechanical damage to apples that occurs without visible skin rupture represents a significant issue during handling and harvesting. The aim of this study was to evaluate the potential for detecting initial internal tissue failure using parameters derived from the deformation curve obtained during a quasi-static penetration test. Particular attention was given to the parameter Pa, defined as the pressure at the yield point, which indicates the onset of structural failure in the tissue. The occurrence of Pa was monitored across five apple genotypes, and the results demonstrated the parameter’s sensitivity to latent internal damage. The parameter Pc, characterising resistance in the later phase of penetration, also showed a strong correlation with fruit bulk density. Significant differences in all mechanical characteristics were observed between the genotypes. The results highlight the potential of these parameters for assessing varietal suitability for mechanised harvesting and sorting. The proposed methodology is readily applicable in the selection of new genotypes within breeding programmes as well as in post-harvest situations. Full article

Based on actual topography measurement data, this study adopts a paraboloid of revolution to characterize asperity geometry and establishes a lateral contact model that accounts for substrate deformation and covers the full elastic–plastic process. The proposed model achieves smooth transitions in contact behavior, effectively avoiding non-physical oscillations and discontinuities in the elastoplastic regime exhibited by existing models. Comparative results with multiple classical models demonstrate that the proposed model maintains strict monotonicity and continuity in predicting contact load, average contact pressure, and contact stiffness. This significantly improves prediction accuracy and physical consistency, providing a more reliable theoretical tool for modeling precision mechanical joint interfaces. Full article

Alginate and chitosan (Ag/Cs) combined form an effective platform to develop biocompatible hydrogels with customizable properties for controlled drug release. Cannabidiol (CBD), a hydrophobic compound with anti-inflammatory and antibacterial effects, represents a powerful strategy to enhance their therapeutic performance. A/Cs hydrogels were produced using the CELLINK^® printer with 12 and 24 mg/mL of CBD. SEM and FTIR were assessed. Viscoelasticity was assessed using oscillatory rheology. Structural strength was evaluated via uniaxial compression. Swelling and absorption were measured gravimetrically under physiological conditions. CBD was successfully incorporated into the 3D-printed A/Cs hydrogel. Increasing the CBD concentration led to mechanical changes such as a dose-dependent decrease in G′ and a slight reduction in the linearity threshold (typically 10–30% from medium loads), while still maintaining G′ > G″. FTIR showed shifts in O–H/N–H and C=O, indicating hydrogen bonding without new reactive bands. Microscopic images revealed moderate pore compaction and increased tortuosity with dose. At higher CBD concentrations, the hydrogel resisted compression but could deform further before failure. Equilibrium swelling and absorption kinetics decreased with increasing dose, resulting in a reduced initial burst and lower water uptake capacity. The CBD-loaded hydrogel provides a mechanically suitable and molecularly stable platform for local drug release in the oral cavity. Full article

Anti-climbing energy absorbers (AEAs) are often installed at the ends of subway vehicles to prevent climbing in the event of a head-on collision or rear-end collision, thereby improving safety performance. To reduce the air resistance of the vehicle during operation, the AEA is usually wrapped with the GFRP head cover. However, the collision behavior of the head cover during a collision requires further research. The effects of mechanical properties of cutting anti-climbing energy absorbers (CAEAs) on the collision behavior of glass fiber reinforced polymer (GFRP) head covers for subway vehicles are investigated in this study. Firstly, the force–displacement curve of the CAEA was obtained through a dynamic impact test, and the finite element (FE) model of the CAEA with a GFRP head cover was constructed and verified. Subsequently, the effects of the four mechanical characteristics of the CAEA (i.e., initial peak crushing force (IPCF), platform force, compaction force, and eccentric height difference) on the collision behavior of the GFRP head cover were systematically analyzed. The results show that the increase in IPCF improves the energy absorption of CAEA, but that damage and stress concentration of the head cover at the moving end also occur. The increase in platform force induced the premature fracture of the GFRP head cover. The collision behavior of the head cover reaches a critical value when the compaction force is between 2500 and 3000 kN. Increasing the eccentric height difference between the anti-climbing teeth weakens the cutting energy absorption efficiency of CAEA and changes its deformation mode. This study can provide important insights into the design and optimization of anti-climbing energy absorbers for subway vehicles, and has important engineering value for improving the durability of the head cover and the collision safety of the vehicle. Full article

Accurate and efficient detection of small and complex surface defects on hot-rolled steel plates remains a significant challenge in industrial quality assurance. Current deep learning detectors often exhibit limitations in detection accuracy and training convergence speed, particularly for small objects, which limits their practical deployment in real-time industrial inspection systems. To overcome these deficiencies, this paper proposes a multi-scale deformable transformer iterative query refinement network (MDT-Net). MDT integrates three key innovations: a Swin Transformer backbone for robust multi-scale feature representation, a deformable attention mechanism to significantly reduce computational complexity and accelerate convergence, and an iterative bounding box refinement strategy for precise localization. Extensive experiments on the NEU-DET dataset demonstrate MDT’s superior performance, achieving 82.7% mAP₅₀. Crucially, MDT significantly outperforms other mainstream detectors in small object detection, reaching an mAP_50:95 of 0.55, and exhibits remarkably faster training convergence. These findings confirm MDT’s effectiveness and robustness for accurate and efficient steel surface defect detection, thereby providing a crucial tool for enhancing sensor-based quality control and offering a promising solution for industrial quality management. Full article

Slate typically contains significant bedding structures and often displays varying mechanical properties under different inclination conditions, with numerous adverse impacts on construction projects. In light of its anisotropic characteristics, a slate foundation pit in Changsha is considered in this study, and uniaxial and triaxial compression tests are initially conducted on slate under various bedding inclination angles. Through these tests, the mechanical parameters of the slate are obtained, and the laws governing the variation in the stress–strain curves and failure modes are analyzed. The results show that the peak strength and elastic modulus present an obvious “U-shaped” variation with the bedding dip angle, reaching the minimum values in the range of 45–60°, and the corresponding failure mode is mainly sliding failure along the bedding plane. The mechanical parameters obtained for slate are input into FLAC3D 6.0 software to simulate a triaxial compressive test of slate, and the calculation results are used to verify the accuracy of the parameters obtained from the tests. Based on these parameters, the foundation pit engineering in the background is simulated in order to analyze the deformation characteristics of the pit under different inclination angles. The simulation results indicate that the foundation pit deformation has significant asymmetry, with larger settlement on the dip side and greater horizontal displacement of the piles. The research findings of this paper can provide a reference for the design and construction of similar slate foundation pit projects. Full article

Owing to their tunable and biocompatible characteristics, collagen- and gelatin-based hydrogels have gained attention in numerous applications, including biomedical, food, pharmaceutical, and environmental. The gelation mechanisms and resulting network structures of collagen and gelatin differ significantly depending on the presence of intra- and intermolecular crosslinks. These differences enable the tailoring of mechanical properties to achieve desired characteristics in the final product. Mechanical gel strength and elasticity determine how effectively hydrogels can mimic natural tissues and respond to deformations. Probing the rheological properties of these gels enables a deeper understanding of their structure, physical attributes, stability, and release profiles. This review provides an in-depth evaluation of the factors affecting the mechanical strength of collagen- and gelatin-based hydrogels, highlighting the influence of co-molecules and the application of physical, chemical, and mechanical treatments. Herewith, it brings insights into how to manipulate the mechanical properties of these gels to improve their end-use functionality. Full article

The mechanical performance of cast iron is strongly governed by the morphology of its graphite phase, yet establishing a quantitative link between microstructure and macroscopic properties remains a challenge. In this study, a three-dimensional finite element method (FEM)-based micromechanical framework is proposed to analyze and predict the mechanical behavior of cast iron with representative graphite morphologies, spheroidal and flake graphite. Realistic representative volume elements (RVEs) are reconstructed based on experimental microstructural characterization and literature-based X-ray computed tomography data, ensuring geometric fidelity and statistical representativeness. Cohesive zone modeling (CZM) is implemented at the graphite/matrix interface and within the graphite phase to simulate interfacial debonding and brittle fracture, respectively. Full-field simulations of plastic strain and stress evolution under uniaxial tensile loading reveal that spheroidal graphite promotes uniform deformation, delayed damage initiation, and enhanced ductility through effective stress distribution and progressive plastic flow. In contrast, flake graphite induces severe stress concentration at sharp tips, leading to early microcrack nucleation and rapid crack propagation along the flake planes, resulting in brittle-like failure. The simulated stress–strain responses and failure modes are consistent with experimental observations, validating the predictive capability of the model. This work establishes a microstructure–property relationship in multiphase alloys through a physics-informed computational approach, demonstrating the potential of FEM-based modeling as a powerful tool for performance prediction and microstructure-guided design of cast iron and other heterogeneous materials. Full article

Fire hazard presents a critical challenge to the structural reliability of underground modular infrastructure. This study examines the fire resistance performance of prefabricated monolithic utility tunnels featuring longitudinal threaded connections. A series of fire exposure tests was conducted on assembled utility tunnel specimens using different bolt materials and thermal conditions, enabling evaluation of fire behavior, deformation behavior, and residual capacity. The observed thermal properties revealed significant temperature gradients across tunnel sections, with the peak internal–external differential reaching 536.8 °C. Post-fire mechanical degradation was evident in reduced stiffness and ductility, and the residual load-bearing capacity declined by up to 12.28% compared to unexposed specimens. Specimens using high-strength threaded bolts demonstrated superior performance compared to stainless steel bolt specimens, exhibiting a 4.67% higher residual capacity and 13.87% less residual deformation. A sequential thermal–mechanical finite element model was developed and calibrated based on experimental results, offering a reliable simulation framework for investigating fire-induced damage and residual strength in modular utility tunnel systems. These findings provide a quantitative basis for fire safety assessment. Full article

This study investigates the mechanical behavior and fatigue performance of orthotropic steel bridge decks, with a focus on rib-to-deck welded connections and the impact of geometric symmetry on stress distribution. Two full-scale models with full-penetration butt welds were tested under static compression loads, yielding failure forces of 27 kN (experimental) and 26 kN (analytical), with only a 3% difference. Finite element simulations using ANSYS 16.1 validated these results and enabled parametric studies. Rib plate thicknesses ranging from 5 mm to 9 mm were analyzed to assess their influence on stress distribution and deformation. The geometric ratio h′/tr, which reflects the symmetry of the trapezoidal rib web, was found to be a critical factor in stress behavior. At h′/tr = 38 (tr = 7 mm), compressive and tensile stresses are balanced, demonstrating a symmetric stress field; at h′/tr = 33 (tr = 8 mm), and fatigue performance at the RDW root drops by 47%. Increasing h′/tr improves fatigue life by increasing the number of load cycles to failure. Stress contours revealed that compressive stress concentrates in the rib plate above the weld toes, while tensile stress localizes at the RDW root. The study highlights how symmetric geometric configurations contribute to balanced stress fields and improved fatigue resistance. Multiple linear regression analysis (SPSS-25) produced predictive equations linking stress values to applied load and geometry, offering a reliable tool for estimating stress without full-scale simulations. These findings underscore the importance of optimizing h′/tr and leveraging structural symmetry to enhance resilience and fatigue resistance in welded joints. This research provides practical guidance for improving the design of orthotropic steel bridge decks and contributes to safer, longer-lasting infrastructure. 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

Electrowetting display technology is increasingly prevalent in modern microfluidic and electronic paper applications, yet it remains susceptible to micro-scale defects such as screen burn-in, charge trapping, and pixel wall deformation. These defects often exhibit low contrast, irregular morphology, and scale diversity, posing significant challenges for conventional detection methods. To address these issues, we propose ASAF-Net, a novel lightweight network incorporating adaptive attention mechanisms for real-time electrowetting defect detection. Our approach integrates three key innovations: a Multi-scale Partial Convolution Fusion Attention module that enhances feature representation with reduced computational cost through channel-wise partitioning; an Adaptive Scale Attention Fusion Pyramid that introduces a dedicated P2 layer for micron-level defect detection across four hierarchical scales; and a Shape-IoU loss function that improves localization accuracy for irregular small targets. Evaluated on a custom electrowetting defect dataset comprising seven categories, ASAF-Net achieves a state-of-the-art mAP@0.5 of 0.982 with a miss detection rate of only 1.5%, while operating at 112 FPS with just 9.82 M parameters. Comparative experiments demonstrate its superiority over existing models such as YOLOv8 and RT-DETR, particularly in detecting challenging defects like charge trapping. This work provides an efficient and practical solution for high-precision real-time quality inspection in electrowetting display manufacturing. Full article