Search Results (1,534)

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

Reinforced soil retaining walls (RSWs) for railways are key subgrade structures that bear cyclic loads from trains, and their long-term durability directly affects railway operation safety. The mechanical behavior of RSWs under cyclic loading has been extensively investigated in previous studies, primarily focusing on seismic conditions or conventional structural configurations. While these works have established fundamental understanding of load transfer mechanisms and deformation patterns, research on their responses to long-term train-induced vibrations, particularly for concrete-block-panel-wrapped RSWs, an improved structure based on traditional concrete-block-panel RSWs, remains limited. To investigate the dynamic responses of the concrete-block-panel-wrapped RSW, a model test was conducted under cyclic loading conditions where the amplitude was 30 kPa and the frequency was 10 Hz. The model size was 3.0 m in length, 1.0 m in width, and 1.8 m in height, incorporating six layers of geogrid. Each layer of geogrid was 2.0 m in length with a vertical spacing of 0.3 m or 0.15 m. The results indicate that as the number of load cycles increases, deformation, acceleration, static and dynamic stresses, and geogrid strain also increase and gradually stabilize, exhibiting only marginal increments thereafter. The maximum horizontal displacement reaches 0.08% of the wall height (H), with horizontal displacement increasing uniformly along the height of the wall. The vertical acceleration in the non-reinforced soil zone is lower than that in the reinforced soil zone. The horizontal dynamic stress acting on the back of the panel remains minimal and is uniformly distributed along the height of the wall. The maximum geogrid strain was found to be 0.88%, corresponding to a tensile stress amounting to 20.33% of its ultimate tensile strength. The predicted failure surface approximates a bilinear configuration, consisting of one line parallel to the wall face at a distance of 0.3H from the back of the soil bags and another line inclined at an angle equal to the soil’s internal friction angle (φ) relative to the horizontal plane. This study has important reference significance for the application of concrete-block-panel-wrapped RSWs in railways. Full article

This study investigates date palm fiber (DPF) and sheep wool hybrid polyester composites with fiber loadings of 0%, 10%, 20%, and 30% by weight, fabricated by compression molding, to develop a sustainable and reliable material system. Experimental data from prior work were modeled using Weibull analysis for reliability evaluation and response surface methodology (RSM) for multi-objective optimization. Weibull statistics fitted a two-parameter distribution to tensile strength and fracture toughness, extracting shape (η) and scale ($\beta$) parameters to quantify variability and failure probability. The analysis showed that 20% hybrid content achieved the highest scale values ($\beta$ = 28.85 MPa for tensile strength and $\beta$ = 15.03 $\mathrm{M}\mathrm{P}\mathrm{a}\sqrt{\mathrm{m}}$ for fracture toughness) and comparatively low scatter (η = 10.39 and 9.2, respectively), indicating superior reliability. RSM quadratic models were developed for tensile strength, fracture toughness, thermal conductivity, acoustic attenuation, and water absorption, and were combined using desirability functions. The RSM optimization was found at 18.97% fiber content with a desirability index of 0.673, predicting 25.89 MPa tensile strength, 14.23 $\mathrm{M}\mathrm{P}\mathrm{a}\sqrt{\mathrm{m}}$ fracture toughness, 0.08 W/m·K thermal conductivity, 20.49 dB acoustic attenuation, and 5.11% water absorption. Overlaying Weibull cumulative distribution functions with RSM desirability surfaces linked probabilistic reliability zones (90–95% survival) to the deterministic optimization peak. This integration establishes a unified framework for designing natural fiber composites by embedding reliability into multi-property optimization. Full article

Background/Objectives: Screw loosening and vertebral fractures remain common after vertebral body tethering (VBT). Because tightening torque sets screw preload, its biomechanical effect warrants explicit modeling. In this paper, a Finite Element (FE) model, supported by ex vivo porcine vertebral tests, was developed and validated that incorporates torque-induced pre-tension to quantify vertebral stress, aiming toward customizable VBT planning. Methods: An FE model with pre-tension and axial extraction failure was parameterized using ex vivo tests on five porcine vertebrae. A laterally inserted surgical screw in each specimen was tightened to $5.9\pm 0.80$ Nm. Axial extraction produced failure loads of $2.1\pm 0.31$ kN. This is also considered in the FE model to validate the failure scenario. Results: Torque alone generated peak von Mises stresses of $16.1\pm 0.86$ MPa (cortical bone 1) and $2.1\pm 0.13$ MPa (trabecular), lower than prior reports. With added axial load, peaks rose to $141.1\pm 0.70$ MPa and $19.7\pm 0.23$ MPa, exceeding typical ranges. However, predicted failure agreed with experiments, showing $0.58$ mm displacement and a conical displacement distribution around the washer. Conclusions: Modeling torque-induced pre-tension is essential to reproduce realistic stress states and anchor failure in VBT. The framework enables patient-specific assessment (bone geometry/density) to recommend safe tightening torques, potentially reducing screw loosening and early fractures. Full article

This study investigates premature fatigue failure in rocker arm gears of large-mining-height shearers operating at alternating ±45° working angles, where insufficient lubrication generates non-uniform thermal -stress fields. In this study, an integrated multiphysics framework combining transient thermal–fluid–structure coupling simulations with fatigue life prediction is proposed. Transient thermo-mechanical coupling analysis simulated dry friction conditions, capturing temperature and stress fields under varying speeds. Fluid–thermal–solid coupling analysis modeled wet lubrication scenarios, incorporating multiphase flow to track oil distribution, and calculated convective heat transfer coefficients at different immersion depths (25%, 50%, 75%). These coupled simulations provided the critical time-varying temperature and thermal stress distributions acting on the gears (Z6 and Z7). Subsequently, these simulated thermo-mechanical loads were directly imported into ANSYS 2024R1 nCode DesignLife to perform fatigue life prediction. Simulations demonstrate that dry friction induces extreme operating conditions, with Z6 gear temperatures reaching over 800 °C and thermal stresses peaking at 803.86 MPa under 900 rpm, both escalating linearly with rotational speed. Lubrication depth critically regulates heat dissipation, where 50% oil immersion optimizes convective heat transfer at 8880 W/m²·K for Z6 and 11,300 W/m²·K for Z7, while 25% immersion exacerbates thermal gradients. Fatigue life exhibits an inverse relationship with speed but improves significantly with cooling. Z6 sustains a lower lifespan, exemplified by 25+ days at 900 rpm without cooling versus 50+ days for Z7, attributable to higher stress concentrations. Based on the multiphysics analysis results, two physics-informed engineering optimizations are proposed to reduce thermal stress and extend gear fatigue life: a staged cooling system using spiral copper tubes and an intelligent lubrication strategy with gear-pump-driven dynamic oil supply and thermal feedback control. These strategies collectively enhance gear longevity, validated via multiphysics-driven topology optimization. Full article

Natural rock materials, containing micro-cracks and pore defects, significantly alter their mechanical behavior. This study investigated fracture interactions of red sandstone containing double close-round holes (diameter: 10 mm; bridge angle: 30°, 45°, 60°, 90°) using acoustic emission (AE) monitoring and the discrete element simulations method (DEM), which was a novel methodology for revealing dynamic failure mechanisms. The uniaxial compression tests showed that hole geometry critically controlled failure modes: specimens with 0° bridge exhibited elastic–brittle failure with intense AE energy releases and large fractures, while 45° arrangements displayed elastic–plastic behaviors with stable AE signal responses until collapse. The quantitative AE analysis revealed that the fracture-type coefficient k had a distinct temporal clustering characteristic, demonstrating the spatiotemporal synchronization of tensile and shear crack initiation and propagation. Furthermore, numerical simulations identified a critical stress redistribution phenomenon, that axial compressive force chains concentrated along the loading axis, forming continuous longitudinal compression zones, while radial tensile dispersion dominated hole peripheries. Crucially, specimens with 45° and 90° bridges induced prominently symmetric tensile fractures (85° to horizontal direction) and shear-dominated failure near junctions. These findings can advance damage prediction in discontinuous geological media and offer direct insights for optimizing excavation sequences and support design in cavern engineering. Full article

While methods like cyclic triaxial testing and p-y model updating theory exist in geotechnical and offshore wind engineering, they have not been systematically applied to solve the specific deformation problems of offshore PV piles. This study investigates a specific offshore photovoltaic (PV) project in Qinhuangdao City, Hebei Province. Initially, field tests of horizontal static load on steel pipe pile foundations were conducted. A finite element model (FEM) of single piles was subsequently developed and validated. Further analysis examined the failure modes, initial stiffness, and ultimate resistance of offshore PV single piles in sandy soil foundations under varying pile diameters and embedment depths. The hyperbolic p-y curve model was modified by incorporating pile diameter size effects and embedment depth considerations. Key findings reveal the following: (1) The predominant failure mechanism of fixed offshore PV monopiles manifests as wedge-shaped failure in shallow soil layers. (2) Conventional API specifications and standard hyperbolic models demonstrate significant deviations in predicting p-y (horizontal soil resistance-pile displacement) curves, whereas the modified hyperbolic model shows good agreement with field measurements and numerical simulations. This research provides critical data support and methodological references for calculating the horizontal bearing capacity of offshore PV steel pipe pile foundations. Full article

Underreamed anchor bolts, as an emerging anchoring element in geotechnical engineering, operate via a fundamentally distinct load transfer mechanism compared with conventional friction type anchors. The accurate and reliable prediction of their ultimate bearing capacity constitutes a pivotal technological impediment to their broader engineering adoption. Firstly, this paper systematically elucidates the constituent mechanisms of underreamed anchor resistance and their progressive load transfer trajectory. Subsequently, in situ full-scale pull-out experiments are leveraged to decompose the load–displacement response throughout its entire evolution. The multi-stage development law and the underlying mechanisms governing the evolution of anchorage characteristics are thereby elucidated. Based on the experimental dataset, a three-dimensional elasto-plastic numerical model is rigorously established. The model delineates, at high resolution, the failure mechanism of surrounding soil mass and the spatiotemporal evolution of its three-dimensional displacement field. A definitive critical displacement criterion for the attainment of the ultimate bearing capacity of underreamed anchors is established. Consequently, analytical models for the ultimate side frictional stress and end-bearing capacity at the limit state are advanced, effectively circumventing the parametric uncertainties inherent in extant empirical formulations. Ultimately, characteristic parameters of the elasto-plastic branch of the load–displacement curve are extracted. An ultimate bearing capacity prognostic framework, founded on an optimized hyperbolic model, is established. Its superior calibration fidelity to the evolving load–displacement response and its demonstrable engineering applicability are rigorously substantiated. Full article

Background: Healthcare 5.0 represents a shift toward intelligent, human-centric care systems. Intensive care units generate vast amounts of data that require real-time decisions, but current decision support systems lack comprehensive frameworks for safe integration of artificial intelligence. Objective: We developed and validated the Learn–Predict–Monitor–Detect–Correct (LPMDC) framework as a methodology for systematic artificial intelligence integration across the critical care workflow. The framework improves predictive analytics, continuous patient monitoring, intelligent alerting, and therapeutic decision support while maintaining essential human clinical oversight. Methods: Framework development employed systematic theoretical modeling integrating Healthcare 5.0 principles, comprehensive literature synthesis covering 2020–2024, clinical workflow analysis across 15 international ICU sites, technology assessment of mature and emerging AI applications, and multi-round expert validation by 24 intensive care physicians and medical informaticists. Each LPMDC phase was designed with specific integration requirements, performance metrics, and safety protocols. Results: LPMDC implementation and aggregated evidence from prior studies demonstrated significant clinical improvements: 30% mortality reduction, 18% ICU length-of-stay decrease (7.5 to 6.1 days), 45% clinician cognitive load reduction, and 85% sepsis bundle compliance improvement. Machine learning algorithms achieved an 80% sensitivity for sepsis prediction three hours before clinical onset, with false-positive rates below 15%. Additional applications demonstrated effectiveness in predicting respiratory failure, preventing cardiovascular crises, and automating ventilator management. Digital twins technology enabled personalized treatment simulations, while the integration of the Internet of Medical Things provided comprehensive patient and environmental surveillance. Implementation challenges were systematically addressed through phased deployment strategies, staff training programs, and regulatory compliance frameworks. Conclusions: The Healthcare 5.0-enabled LPMDC framework provides the first comprehensive theoretical foundation for systematic AI integration in critical care while preserving human oversight and clinical safety. The cyclical five-phase architecture enables processing beyond traditional cognitive limits through continuous feedback loops and system optimization. Clinical validation demonstrates measurable improvements in patient outcomes, operational efficiency, and clinician satisfaction. Future developments incorporating quantum computing, federated learning, and explainable AI technologies offer additional advancement opportunities for next-generation critical care systems. Full article

Left ventricular non-compaction cardiomyopathy (LVNC) is an uncommon myocardial phenotype characterized by prominent trabeculae and deep blood-filled recesses. The expanding use of cardiac magnetic resonance (CMR) has increased detection, yet uncertainty persists about whether LVNC is a distinct disease or a phenotype that overlaps with other cardiomyopathies. LVNC expression reflects the interplay among genotype, sex, ancestry, and hemodynamic load and thus serves as a model for precision cardiology. We conducted a narrative review of literature published between January 2000 and April 2025 in major databases. We included clinical studies with at least 10 patients, meta-analyses, reviews, and consensus statements addressing pathophysiology, genetics, diagnosis, prognosis, and treatment. Sarcomeric variants account for a substantial fraction of cases and connect LVNC with dilated and hypertrophic cardiomyopathies. Echocardiographic and CMR criteria identify the phenotype but blur the boundary between physiological and pathological hypertrabeculation. Fibrosis on late gadolinium enhancement and systolic dysfunction are consistently associated with worse outcomes. Current management largely adapts heart-failure strategies, including neurohormonal blockade, SGLT2 inhibitors, and implantable cardioverter-defibrillators in selected high-risk patients. Optimal care integrates clinical, imaging, and genetic information. The lack of universal diagnostic criteria highlights the need for prospective studies and consensus to standardize diagnosis and treatment. Future algorithms that combine multi-omics, quantitative imaging, and AI-based risk prediction could individualize surveillance, pharmacotherapy, and device therapy. Full article

When jointed rock masses are in a high-stress environment, the roughness of the joints is the key factor controlling their shear strength. Their loading behavior is also different from the constant normal load (CNL) conditions controlled in conventional laboratories; rather, they follow the constant normal stiffness (CNS) conditions. To investigate the effects of normal stiffness and roughness on the shear mechanical properties of unfilled joint surfaces, shear tests were simulated using PFC3D (5.0) software under CNS conditions. The effects of normal stiffness of 0 (constant normal stress of 4 MPa), 0.028 GPa/m (low normal stiffness), 0.28 GPa/m (medium normal stiffness), and 2.8 GPa/m (high normal stiffness), and joint roughness coefficients (JRC) of 2~4 (low roughness), 10~12 (medium roughness), and 18~20 (high roughness) on the shear stress, normal stress, normal deformation, surface resistance index, and block failure characteristics of the joint surface were obtained. The results indicate that for different combinations of normal stiffness—JRC—the shear simulation process primarily exhibits three deformation stages: linear stage, yield stage, and post-peak stage. Shear stress increases initially and then decreases as shear displacement increases. When normal stiffness is no less than 0.28 GPa/m, both normal stress and JRC increase gradually with increasing JRC and normal stiffness. When the normal stiffness is no greater than 0.028 GPa/m, the normal stress shows no significant change. The normal displacement changes from “shear contraction” to “shear expansion” with increasing shear displacement and from positive to negative values while the displacement gradually increases; the maximum normal displacement decreases with increasing normal stiffness and increases with increasing JRC. The peak SRI value increases with increasing JRC and decreases with increasing normal stiffness. As normal stiffness increases, the number of tensile cracks for JRC 2~4 first decreases and then increases, while the number of shear cracks gradually increases; for JRC 10~12 and 18~20, both the number of shear cracks and tensile cracks increase with increasing normal stiffness. This paper simulates the actual mechanical environment of deep underground joints to expound the influence of normal stiffness and joint roughness on the stability of deep rock masses. The research results can provide certain theoretical references for predicting the stability of deep surrounding rocks and the stress of support structures. Full article

This study investigates the axial performance of continuous helix screw piles compared to helical piles through full-scale compression and tension load testing in layered soils. Twenty-three piles were installed and tested. The results demonstrate that screw piles can achieve considerable axial capacity with lower installation torque than helical piles, particularly under tensile loading. The capacity-torque relationship for screw piles was more consistent across both compression and tension, likely due to reduced soil disturbance from the smaller helix projection. Strain gauge measurements indicated that screw piles act primarily as friction piles with the threaded shaft carrying most of the load, especially in stiff clay. On the other hand, the smooth portion of the pile shaft contributed only marginally to resistance in compression and none in tension. The calculated capacity based on theoretical equations aligned well with field results in compression, with screw piles best represented by cylindrical shear failure in sand and a combination of cylindrical shear and individual bearing failure in clay. However, there is greater variability between calculated and measured uplift capacity, possibly due to soil disturbance effects. Additionally, the commonly used helix spacing ratio (S/D) was found to be less applicable to screw piles in predicting failure mode due to their smaller shaft-to-helix diameter difference. Full article

In recent years, there has been an increase in the frequency of severe weather events (like hurricanes). These events are responsible for most power outages in power distribution systems (PDSs). Particularly susceptible to storms are overhead PDSs. In this study, the dynamic Bayesian network (DBN)-based failure model was developed for different hurricane scenarios to predict the line failure of overhead lines. Based on the outcomes of the DBN model, a service restoration model was formulated to maximize restored loads and minimize power losses using Particle Swarm Optimization (PSO)-based distributed generation (DG) integration and system reconfiguration. Three different case studies based on the IEEE 33 bus system were conducted. The overhead line failure prediction and service restoration model findings were further used to calculate resilience metrics. With reconfiguration the load restored from 90.3% to 100% for Case 1 and from 34.994% to 80.35% for Case 2. However, for Case 3, reconfiguration alone was not sufficient to show any improvement in performance. On the other hand, DG integration successfully restored load to 100% in all three cases. These results demonstrated that the combined DBN-based failure modeling and PSO-driven optimal restoration strategy under hurricane-induced disruptions can effectively strengthen system resilience. Full article

Current understanding of the load-transfer mechanism in the skeletal contact state of asphalt mixtures and its influence on macroscopic mechanical properties remains insufficient. This knowledge gap leads to difficulties in accurately predicting the performance of designed mixtures, thereby restricting the service life of asphalt pavements and the sustainable development of road engineering. This study investigated the skeletal contact characteristics, coarse aggregate movement, and crack propagation of three asphalt mixture types—Stone Mastic Asphalt (SMA), Asphalt Concrete (AC), and Open-Graded Friction Course (OGFC)—under loading. The methodology incorporated Computed Tomography (CT) technology, a Voronoi diagram-based skeletal contact evaluation method, and discrete element numerical simulation. The research aimed to elucidate the influence mechanisms of different skeletal structures on macroscopic performance and to validate the efficacy of the skeletal contact evaluation method. The findings revealed that under splitting load, the tensile stress contact force chains within the asphalt mixture’s skeleton were predominantly distributed along both sides of the specimen’s central axis. For all three gradations, compressive stress contact force chains (points) accounted for over 65% of the total, indicating that the asphalt mixture skeleton primarily bore and transmitted compressive stresses. The interlocking structure formed by coarse aggregates significantly enhanced the stability of the asphalt mixture skeleton, reduced its displacement under load, and improved the mixture’s resistance to cracking. In the three gradations, shear stress-induced cracks outnumbered those caused by tensile stress, with shear stress cracks accounting for over 55% of the total cracks. This suggests that under splitting load, cracks resulting from shear failure were more prevalent than those from tensile failure. SMA-20 demonstrated the best crack resistance, followed by AC-20, while OGFC-20 performed the poorest. These conclusions are consistent with the results of the Voronoi diagram-based skeletal contact evaluation, confirming the correlation between the contact conditions of the asphalt mixture skeleton and its mechanical performance. Specifically, inadequate skeletal contact leads to a significant deterioration in mechanical properties. The research results elucidate the influence of skeletal contact characteristics with different gradations on both mesoscopic features and macroscopic mechanical behavior, providing a crucial basis for optimizing asphalt mixture design. Full article

The floating oil seal (FOS) is a critical component in coal mining machinery, where frictional wear and high stress on the O-ring can lead to oil leakage and eventual FOS failure, significantly impairing equipment performance. To address this issue, this study proposes a novel ceramic-coated floating oil seal (NCCFOS) composite structure that enhances wear resistance without modifying the existing sealing cavity configuration. A two-dimensional axisymmetric finite element model of the NCCFOS was developed based on the Mooney–Rivlin constitutive model, considering the O-ring assembly process for improved accuracy. The model was analyzed under oil pressure loading, with parametric studies examining the influence of oil pressure, assembly clearance, and material hardness on O-ring stress, contact pressure, and frictional stress distribution in the floating seal ring. The results demonstrate that accounting for the assembly process yielded more realistic stress predictions compared to conventional modeling approaches. The NCCFOS design effectively mitigated stress concentrations, reduced O-ring wear, and extended fatigue life, offering a practical solution for enhancing the reliability of coal mining machinery seals. Full article