Search Results (1,588)

Normal concrete exhibits poor resistance to chloride penetration, often leading to reinforcement corrosion and premature structural failure. In contrast, ultra-high-performance concrete (UHPC) demonstrates superior resistance to corrosion caused by chloride salts. The chloride diffusion behaviour of UHPC was investigated via long-term immersion (LTI) and rapid chloride migration (RCM) tests. Additionally, this study presents the first development of a time-dependent diffusion model for UHPC under chloride corrosion, as well as the proposal of a performance-based design method for calculating the protective layer thickness. Results show that the incorporation of steel fibers reduced the chloride diffusion coefficient (D) by 37.9%. The free chloride content (FCC) in UHPC increased by 92.0% at 2 mm after 300 d of the action of LTI. D decreased by up to 91.0%, whereas the surface chloride concentration (C_s) increased by up to 92.5% under the action of LTI. The time-dependent models of D and C_s followed power and logarithmic functions, respectively. An increase in UHPC surface temperature, relative humidity, and tensile stress ratio significantly diminishes the chloride resistance of UHPC. The minimum UHPC protective layer thicknesses required for UHPC-HPC composite beams with design service lives of 100 years, 150 years, and 200 years are 30 mm, 37 mm, and 43 mm, respectively. Full article

Explosions, including those from war weapons, terrorist attacks, etc., can lead to damage and overall collapse of bridges. However, there are no clear guidelines for anti-blast design and protective measures for bridges under blast loading in current bridge design specifications. With advancements in intelligent construction, precast segmental bridge piers have become a major trend in social development. There is a lack of full understanding of the anti-blast performance of precast segmental bridge piers. To study the engineering calculation method for blast load acting on a typical precast segmental reinforced concrete (RC) pier in near-field explosions, an air explosion test of the precast segmental RC pier is firstly carried out, then a fluid–structure coupling numerical model of the precast segmental RC pier is established and the interaction between the explosion shock wave and the precast segmental RC pier is discussed. A numerical simulation of the precast segmental RC pier in a near-field explosion is conducted based on a reliable numerical model, and the distribution of the blast load acting on the precast segmental RC pier in the near-field explosion is analyzed. The results show that the reflected overpressure on the pier and the incident overpressure in the free field are reliable. The simulation results are basically consistent with the experimental results (with a relative error of less than 8%), and the fluid–structure coupling model is reasonable and reliable. The explosion shock wave has effects of reflection and circulation on the precast segmental RC pier. In the near-field explosion, the back and side blast loads acting on the precast segmental RC bridge pier can be ignored in the blast-resistant design. The front blast loads can be simplified and equalized, and a blast-resistant design load coefficient (1, 0.2, 0.03, 0.02, and 0.01) and a calculation formula of maximum equivalent overpressure peak value (applicable scaled distance [0.175 m/kg^1/3, 0.378 m/kg^1/3]) are proposed, which can be used as a reference for the blast-resistant design of precast segmental RC piers. Full article

Earthquakes can cause significant damage to structures, resulting in considerable financial and social losses. Enhancing the seismic capacity of existing structures through retrofitting is essential. Traditional seismic retrofitting techniques, such as reinforced concrete (RC) jacketing and steel jacketing, primarily aim to increase structural resistance. But RC jacketing is intrusive and increases mass and stiffness, steel jacketing increases cost and demands careful detailing and both approaches are often inadequate for addressing the dynamic complexities of seismic loading. As an alternative, base isolation systems provide a promising solution by concentrating deformation and energy dissipation within isolation bearings, thereby protecting the superstructure from seismic forces. This study evaluates the effectiveness of base isolation compared with conventional retrofitting methods in enhancing the seismic performance of existing structures. The experimental program included cyclic testing of four RC frame structures: one control specimen and three others retrofitted with RC jacketing, steel jacketing, and lead rubber bearings (LRB). The results indicate that the base-isolated specimen demonstrates superior energy dissipation capacity due to the favorable deformation characteristics of the LRB. Moreover, structural damage is redirected from the original columns to the newly installed transition beams, effectively preserving the integrity of the primary structure. These findings highlight the advantages of base isolation in improving seismic performance and provide valuable experimental evidence supporting its application in the retrofitting of existing structures. Full article

Atmospheric turbulence severely limits the stability and reliability of free-space optical (FSO) uplinks by inducing wavefront distortions and random intensity fluctuations. This study investigates the use of reinforcement learning (RL) with beacon-based feedback for adaptive beam shaping in a spatial light modulator (SLM)-controlled FSO link. The RL agent dynamically adjusts phase patterns to maximize received signal strength, while the beacon channel provides turbulence estimates that guide the optimization process. Experiments under low, moderate, and high turbulence levels demonstrate that incorporating beacon feedback can enhance link stability in severe conditions, reducing signal variability and suppressing extreme fluctuations. In low-turbulence scenarios, the performance is comparable to non-feedback operation, whereas under high turbulence, beacon-assisted control consistently achieves lower coefficients of variation and improved bit error rate (BER) performance. Under high turbulence replay experiments—where the best-performing RL-learned phase patterns are reapplied without learning—further show that configurations trained with feedback retain robustness, even without real-time turbulence measurements under high turbulence. These results highlight the potential of integrating contextual feedback with RL to achieve turbulence-resilient and stable optical uplinks in dynamic atmospheric environments. Full article

The paper presents a comparative analysis of various machine learning algorithms for the classification of epoxy composites reinforced with basalt fiber and modified with inorganic fillers. The classification is based on key thermophysical characteristics, in particular, the mass fraction of the filler, temperature, and thermal conductivity coefficient. A dataset of 16,056 interpolated samples was used to train and evaluate more than a dozen models. Among the tested algorithms, the MLP neural network model showed the highest accuracy of 99.7% and balanced classification metrics F1-measure and G-Mean. Ensemble methods, including XGBoost, CatBoost, ExtraTrees, and HistGradientBoosting, also showed high classification accuracy. To interpret the results of the MLP model, SHAP analysis was applied, which confirmed the predominant influence of the mass fraction of the filler on decision-making for all classes. The results of the study confirm the high effectiveness of machine learning methods for recognizing filler type in composite materials, as well as the potential of interpretable AI in materials science tasks. Full article

This study investigates the fabrication and performance of a novel composite material by infiltrating SnSb11Cu6 babbitt alloy into an open-cell AlSn6Cu-SiC matrix. The composites, produced via a multi-stage liquid-state processing route, were comprehensively characterized for their microstructural, mechanical, and tribological properties. The inclusion of 5 wt.% silicon carbide reinforcement resulted in a significant improvement in tribological performance under dry-sliding conditions. Specifically, the reinforced composite exhibited a 24.8% reduction in wear and a 10.8% reduction in the coefficient of friction compared to its unreinforced counterpart. Crucially, this enhancement in wear resistance was achieved while the bulk compressive mechanical properties and ductile deformation behavior remained virtually identical to the unreinforced material. Microstructural analysis confirmed that the high-hardness SiC particles act as primary load-bearing agents, shielding the softer metallic matrix from severe wear. These findings demonstrate the successful development of a high-performance composite with enhanced tribological durability without a mechanical trade-off, making it a promising candidate for advanced bearing applications. Full article

In the marine salt spray environment, steel fiber reinforced concrete (SFRC) structures are often subjected to accelerated durability degradation, primarily due to chloride-induced corrosion. To address this issue, polypropylene (PP) fibers were incorporated to partially replace steel fibers in the formulation of hybrid fiber reinforced concrete (HFRC), thereby enhancing its resistance to chloride corrosion. The results demonstrate that all HFRC mixtures achieved a compressive strength of approximately 65 MPa at 28 d. After 200 d of salt spray exposure, the compressive strength of the HFRC containing PP fibers decreased at a significantly slower rate than that of the control group (M0) incorporating sole steel fibers, with the former still meeting the high-strength concrete standard (>60 MPa). Regardless of the exposure duration to salt spray, the wave velocity of the HF series remained higher than that of M0. This suggests that the PP fibers play a significant role in preserving the matrix’s compactness, effectively mitigating deterioration caused by chloride corrosion. Furthermore, after 200 d of exposure, the peak chloride content, critical corrosion depth, and chloride diffusion coefficient of HF2 were 0.58%, 16 mm, and 1.24 × 10⁻¹² m²/s, respectively, all of which were lower than those of the other specimens. This demonstrates that incorporating 0.3 vol% PP fibers most effectively enhances the chloride corrosion resistance of HFRC. Full article

Basalt fiber-reinforced polymer (BFRP) composites are increasingly utilized in photovoltaic mounting systems due to their excellent mechanical properties and durability. Bolted connections, valued for their simplicity, ease of installation, and effective load transfer, are widely employed for joining composite components. An orthogonal experimental design was adopted to investigate the effects of key parameters—including bolt end distance, number of bolts, bolt material, bolt diameter, preload, and connection length—on the load-bearing performance of three bolted BFRP plate configurations: lap joint (DJ), single lap joint (DP), and double lap joint (SP). Test results showed that the DJ connection exhibited the highest average tensile load capacity, exceeding those of the SP and DP connections by 45.3% and 50.2%, respectively. This superiority is attributed to the DJ specimen’s longer effective shear length and greater number of load-bearing bolts. Conversely, the SP connection demonstrated the largest average peak displacement, with increases of 29.7% and 52.9% compared to the DP and DJ connections. The double-sided constraint in the SP configuration promotes more uniform preload distribution and enhances shear deformation capacity. Orthogonal sensitivity analysis further revealed that the number of bolts and preload magnitude significantly influenced the ultimate tensile load capacity across all connection types. Finally, a calculation model for the tensile load capacity of bolted BFRP connections was established, incorporating a friction decay coefficient (α) and shear strength (τ). This model yields calculated errors under 15% and is applicable to shear slip-dominated failure modes, thereby providing a parametric basis for optimizing the tensile design of bolted BFRP joints. Full article

This research evaluates the stabilization of a clay collected from the northern expansion zone of Cartagena de Indias, Colombia. Laboratory analyses, including particle size distribution, Atterberg limits, compaction, specific gravity, and XRF/XRD, classified the soil as a highly plastic clay (CH) with moderate dispersivity, as confirmed by pinhole and crumb tests. The soil was treated with 3–9% lime, with and without the addition of NaCl (0% and 2%), and tested for unconfined compressive strength (q_u), small-strain stiffness (G_o), and microstructural properties under curing periods of 14 and 28 days at two compaction densities. Results showed that lime significantly improved mechanical behavior, while the inclusion of NaCl further enhanced q_u (up to 185%) and G_o (up to 3-fold), particularly at higher lime contents and curing times. Regression models demonstrated that both q_u and G_o follow power-type relationships with the porosity-to-lime index, with consistent exponents (−4.75 and −5.23, respectively) and high coefficients of determination (R² > 0.79). Normalization of the data yielded master curves with R² values above 0.90, confirming the robustness of the porosity-to-lime framework as a predictive tool. The G_o/q_u ratio obtained (3737.4) falls within the range reported for cemented geomaterials, reinforcing its relevance for comparative analysis. SEM observations revealed the transition from a porous, weakly aggregated structure to a dense matrix filled with C–S–H and C–A–H gels, corroborating the macro–micro correlation. Overall, the combined use of lime and NaCl effectively converts dispersive clays into non-dispersive, mechanically improved geomaterials, providing a practical and sustainable approach for stabilizing problematic coastal soils in tropical environments. Full article

Understanding the dynamics of fire propagation is essential in improving predictive models and developing effective fire management strategies. This study applies computer vision techniques to complement traditional fire behaviour modelling. We employ the Segment Anything Model to achieve the accurate segmentation of experimental fire videos, enabling the frame-by-frame segmentation of fire perimeters, quantification of the rate of spread in multiple directions, and explicit analysis of slope effects. Our laboratory experiments reveal that the ROS increases exponentially with slope, but with coefficients differing from those prescribed in the Canadian Fire Behaviour Prediction System, reflecting differences in field conditions. Complementary field data from prescribed burns in coniferous fuels (C-7) further demonstrate that slope effects vary under operational conditions, suggesting field-dependent dynamics not fully captured by existing deterministic models. Our experiments show that, even under controlled laboratory conditions, substantial variability in spread rate is observed, underscoring the inherent stochasticity of fire spread. Together, these findings highlight the value of vision-based perimeter extraction in generating precise spread data and reinforce the need for probabilistic modelling approaches that explicitly account for uncertainty and emergent dynamics in fire behaviour. Full article

With the increasing demand for soil modification technologies in the field of civil engineering, this study employed cement-stabilized soil and MBER (Material Becoming Earth into Rock) stabilized soil as controls to investigate the modification effects of an N-MBER (nanosilica reinforced MBER) stabilizer on the mechanical properties and microstructure of loess. The mechanical and water stability characteristics of N-MBER-stabilized loess under varying moisture contents and compaction degrees were analyzed through unconfined compressive strength (UCS) tests, softening coefficient tests, falling-head permeability tests, and wet–dry cycle tests. Combined with scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), and nuclear magnetic resonance (NMR) techniques, the underlying mechanism of the N-MBER stabilizer in loess stabilization was thoroughly revealed. The results indicate that the N-MBER stabilizer significantly enhances the UCS and softening coefficient of loess. Particularly, under conditions of 28-day curing, a moisture content of 16%, and a compaction degree of 1, the compressive strength achieves a local optimum value of 3.68 MPa. Compared to soils stabilized with MBER stabilizers and cement stabilizers, the N-MBER-stabilized loess exhibits superior water resistance and microstructural density, with a significant reduction in the proportion of pore defects. Specifically, after five wet–dry cycles at a curing age of 28 days, the strength loss rates for MBER-stabilized soil and cement-stabilized soil were 24.4% and 27.54%, respectively, while that for N-MBER-stabilized soil was 18.23%, demonstrating its enhanced water resistance. Additionally, compared to cement-stabilized soil, the N-MBER-stabilized soil exhibited a 21.63% reduction in total pore number, with a 41.64% reduction specifically in large pores. The extremely small particle size and large specific surface area of the nanomaterial enable more effective interactions with soil particles, promoting hydration reactions. The resulting ettringite (AFt) and three-dimensional networked C-S-H gel tightly interweave with soil particles, forming a stable cemented structure. Compared to traditional concrete roads, stabilized soil roads enable the utilization of locally available materials and demonstrate a significant cost advantage. This study provides theoretical support and experimental evidence for the application of nanomaterials in loess improvement engineering. Full article

The current engineering structural design code employs a direct probability design method based on the Taylor series expansion of the performance function at verification points, retaining only linear terms. This approach ignores the curvature and other nonlinear properties of the performance function, leading to insufficient accuracy. To address the deficiencies of the current design method, this paper develops an approximate probability design method that considers the curvature of the limit state surface, integrating it with the second-order moment theory based on the direct probability design method. Using a simply supported plate as a representative example, this paper systematically compares the performance of the proposed design method with the direct probability design method, the partial coefficient method, and the design value method in reinforcement design. The reinforcement areas calculated by the four methods are similar, confirming the correctness and practicality of the proposed method for engineering applications. The accuracy of the design outcomes from the various methods is validated through Monte Carlo simulation. The results indicate that the method proposed in this paper exhibits a high accuracy, with the relative errors of the reliability indices in the two examples being 0.346% and 0.228%, respectively—significantly lower than those of the direct probability design method (2.919% and 0.769%, respectively). This underscores the effectiveness and substantial benefits of the proposed method in structural reliability design, offering a dependable, highly accurate, and economically viable design tool for engineering applications. Full article

This study examines the incorporation of chopped glass fiber and nano-silica into epoxy, focusing on their effects on the tribological and mechanical properties. Three reinforcement ratios (1 wt.%, 3 wt.%, and 5 wt.%) were analyzed by scratch tests and profilometric analysis. The coefficient of friction (COF), scratch depth, and scratch width values of the unreinforced epoxy resin were measured as 0.45, 37.73 µm and 479 µm, respectively. The addition of glass fibers contributed to improved scratch performance by restricting material removal and stabilizing groove morphology, although higher fiber ratios caused an increase in COF. The results indicated that nano-silica increased scratch resistance with a COF of 0.42 at 5 wt.%, giving a scratch depth of 19.92 µm and a scratch width of 166 µm. Glass fiber also improved scratch performance, although there were high COF values for higher ratios, which could be due to the aggregation effect of the fibers. Statistical validation of the results was carried out through the Taguchi method and ANOVA analyses. These analyses showed that reinforcement type and ratio played an important role in scratch behavior. SEM analyses of worn surfaces showed that nano-silica can dissipate stress and minimize plastic deformation to yield improved scratch morphology. Overall, the results emphasize the complementary role of glass fiber and nano-silica reinforcements in improving the scratch resistance of epoxy resin for industrial applications. Full article

WSNs consist of numerous energy-constrained Sensor Nodes (SNs), making energy efficiency a critical challenge. This paper presents a novel multipath routing model designed to enhance network lifetime by simultaneously optimizing energy consumption, node connectivity, and transmission distance. The model employs an Improved Particle Swarm Optimization (IPSO) algorithm to dynamically determine the optimal weight coefficients of a cost function that integrates three parameters: residual energy, link reliability, and buffer capacity. A compressed Bloom filter is incorporated to improve packet transmission efficiency and reduce error rates. Simulation experiments conducted in the NS2 environment show that the proposed approach significantly outperforms existing protocols, including Reinforcement Learning Q-Routing Protocol (RL-QRP), Low Energy Adaptive Clustering Hierarchical (LEACH), On-Demand Distance Vector (AODV), Secure and Energy-Efficient Multipath (SEEM), and Energy Density On-demand Cluster Routing (EDOCR), achieving a 7.45% reduction in energy consumption and maintaining a higher number of active nodes over time. Notably, the model sustains 19 live nodes at round 800, whereas LEACH and APTEEN experience complete node depletion by that point. This adaptive, energy-aware routing strategy improves reliability, prolongs operational lifespan, and enhances load balancing, making it a promising solution for real-world WSN applications. Full article

This study tackles the issue of ensuring secure communications in vehicular ad hoc networks (VANETs) under dynamic eavesdropping threats, where eavesdroppers adaptively reposition to intercept transmissions. We introduce a scheme utilizing a partitioned reconfigurable intelligent surface (RIS) to assist in the joint transmission of confidential signals and artificial noise (AN) from a source station. The RIS is divided into segments: one enhances legitimate signal reflection toward the intended vehicular receiver, while the other directs AN toward eavesdroppers to degrade their reception. To maximize secrecy performance in rapidly changing environments, we introduce a joint optimization framework integrating meta-learning for RIS partitioning and reinforcement learning (RL) for reflection matrix optimization. The meta-learning component rapidly determines the optimal RIS partitioning ratio when encountering new eavesdropping scenarios, leveraging prior experience to adapt with minimal data. Subsequently, RL is employed to dynamically optimize both beamforming vectors as well as RIS reflection coefficients, thereby further improving the security performance. Extensive simulations demonstrate that the suggested approach attain a 28% higher secrecy rate relative to conventional RIS-assisted techniques, along with more rapid convergence compared to traditional deep learning approaches. This framework successfully balances signal enhancement with jamming interference, guaranteeing robust and energy-efficient security in highly dynamic vehicular settings. Full article