Unmanned Aerial Vehicles(UAVs)have become integral components in smart city infrastructures,supporting applications such as emergency response,surveillance,and data collection.However,the high mobility and dynamic top...Unmanned Aerial Vehicles(UAVs)have become integral components in smart city infrastructures,supporting applications such as emergency response,surveillance,and data collection.However,the high mobility and dynamic topology of Flying Ad Hoc Networks(FANETs)present significant challenges for maintaining reliable,low-latency communication.Conventional geographic routing protocols often struggle in situations where link quality varies and mobility patterns are unpredictable.To overcome these limitations,this paper proposes an improved routing protocol based on reinforcement learning.This new approach integrates Q-learning with mechanisms that are both link-aware and mobility-aware.The proposed method optimizes the selection of relay nodes by using an adaptive reward function that takes into account energy consumption,delay,and link quality.Additionally,a Kalman filter is integrated to predict UAV mobility,improving the stability of communication links under dynamic network conditions.Simulation experiments were conducted using realistic scenarios,varying the number of UAVs to assess scalability.An analysis was conducted on key performance metrics,including the packet delivery ratio,end-to-end delay,and total energy consumption.The results demonstrate that the proposed approach significantly improves the packet delivery ratio by 12%–15%and reduces delay by up to 25.5%when compared to conventional GEO and QGEO protocols.However,this improvement comes at the cost of higher energy consumption due to additional computations and control overhead.Despite this trade-off,the proposed solution ensures reliable and efficient communication,making it well-suited for large-scale UAV networks operating in complex urban environments.展开更多
Effective partitioning is crucial for enabling parallel restoration of power systems after blackouts.This paper proposes a novel partitioning method based on deep reinforcement learning.First,the partitioning decision...Effective partitioning is crucial for enabling parallel restoration of power systems after blackouts.This paper proposes a novel partitioning method based on deep reinforcement learning.First,the partitioning decision process is formulated as a Markov decision process(MDP)model to maximize the modularity.Corresponding key partitioning constraints on parallel restoration are considered.Second,based on the partitioning objective and constraints,the reward function of the partitioning MDP model is set by adopting a relative deviation normalization scheme to reduce mutual interference between the reward and penalty in the reward function.The soft bonus scaling mechanism is introduced to mitigate overestimation caused by abrupt jumps in the reward.Then,the deep Q network method is applied to solve the partitioning MDP model and generate partitioning schemes.Two experience replay buffers are employed to speed up the training process of the method.Finally,case studies on the IEEE 39-bus test system demonstrate that the proposed method can generate a high-modularity partitioning result that meets all key partitioning constraints,thereby improving the parallelism and reliability of the restoration process.Moreover,simulation results demonstrate that an appropriate discount factor is crucial for ensuring both the convergence speed and the stability of the partitioning training.展开更多
Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrain...Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.展开更多
At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown ...At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown and complex environments,this paper proposes an Attention-Enhanced Dueling Deep Q-Network(ADDueling DQN),which integrates a multi-head attention mechanism and a prioritized experience replay strategy into a Dueling-DQN reinforcement learning framework.A multi-objective reward function,centered on energy efficiency,is designed to comprehensively consider path length,terrain slope,motion smoothness,and obstacle avoidance,enabling optimal low-energy trajectory generation in 3D space from the source.The incorporation of a multihead attention mechanism allows the model to dynamically focus on energy-critical state features—such as slope gradients and obstacle density—thereby significantly improving its ability to recognize and avoid energy-intensive paths.Additionally,the prioritized experience replay mechanism accelerates learning from key decision-making experiences,suppressing inefficient exploration and guiding the policy toward low-energy solutions more rapidly.The effectiveness of the proposed path planning algorithm is validated through simulation experiments conducted in multiple off-road scenarios.Results demonstrate that AD-Dueling DQN consistently achieves the lowest average energy consumption across all tested environments.Moreover,the proposed method exhibits faster convergence and greater training stability compared to baseline algorithms,highlighting its global optimization capability under energy-aware objectives in complex terrains.This study offers an efficient and scalable intelligent control strategy for the development of energy-conscious autonomous navigation systems.展开更多
While reinforcement learning-based underwater acoustic adaptive modulation shows promise for enabling environment-adaptive communication as supported by extensive simulation-based research,its practical performance re...While reinforcement learning-based underwater acoustic adaptive modulation shows promise for enabling environment-adaptive communication as supported by extensive simulation-based research,its practical performance remains underexplored in field investigations.To evaluate the practical applicability of this emerging technique in adverse shallow sea channels,a field experiment was conducted using three communication modes:orthogonal frequency division multiplexing(OFDM),M-ary frequency-shift keying(MFSK),and direct sequence spread spectrum(DSSS)for reinforcement learning-driven adaptive modulation.Specifically,a Q-learning method is used to select the optimal modulation mode according to the channel quality quantified by signal-to-noise ratio,multipath spread length,and Doppler frequency offset.Experimental results demonstrate that the reinforcement learning-based adaptive modulation scheme outperformed fixed threshold detection in terms of total throughput and average bit error rate,surpassing conventional adaptive modulation strategies.展开更多
Understanding the reinforcement effect of the newly developed prestressed reinforcement components(PRCs)(a system composed of prestressed steel bars(PSBs),protective sleeves,lateral pressure plates(LPPs),and anchoring...Understanding the reinforcement effect of the newly developed prestressed reinforcement components(PRCs)(a system composed of prestressed steel bars(PSBs),protective sleeves,lateral pressure plates(LPPs),and anchoring elements)is technically significant for the rational design of prestressed subgrade.A three-dimensional finite element model was established and verified based on a novel static model test and utilized to systematically analyze the influence of prestress levels and reinforcement modes on the reinforcement effect of the subgrade.The results show that the PRCs provide additional confining pressure to the subgrade through the diffusion effect of the prestress,which can therefore effectively improve the service performance of the subgrade.Compared to the unreinforced conventional subgrades,the settlements of prestressreinforced subgrades are reduced.The settlement attenuation rate(Rs)near the LPPs is larger than that at the subgrade center,and increasing the prestress positively contributes to the stability of the subgrade structure.In the multi-row reinforcement mode,the reinforcement effect of PRCs can extend from the reinforced area to the unreinforced area.In addition,as the horizontal distance from the LPPs increases,the additional confining pressure converted by the PSBs and LPPs gradually diminishes when spreading to the core load bearing area of the subgrade,resulting in a decrease in the Rs.Under the singlerow reinforcement mode,PRCs can be strategically arranged according to the local areas where subgrade defects readily occurred or observed,to obtain the desired reinforcement effect.Moreover,excessive prestress should not be applied near the subgrade shoulder line to avoid the shear failure of the subgrade shoulder.PRCs can be flexibly used for preventing and treating various subgrade defects of newly constructed or existing railway lines,achieving targeted and classified prevention,and effectively improving the bearing performance and deformation resistance of the subgrade.The research results are instructive for further elucidating the prestress reinforcement effect of PRCs on railway subgrades.展开更多
Lunar core samples are the key materials for accurately assessing and developing lunar resources.However,the difficulty of maintaining borehole stability in the lunar coring process limits the depth of lunar coring.He...Lunar core samples are the key materials for accurately assessing and developing lunar resources.However,the difficulty of maintaining borehole stability in the lunar coring process limits the depth of lunar coring.Here,a strategy of using a reinforcement fluid that undergoes a phase transition spontaneously in a vacuum environment to reinforce the borehole is proposed.Based on this strategy,a reinforcement liquid suitable for a wide temperature range and a high vacuum environment was developed.A feasibility study on reinforcing the borehole with the reinforcement liquid was carried out,and it is found that the cohesion of the simulated lunar soil can be increased from 2 to 800 kPa after using the reinforcement liquid.Further,a series of coring experiments are conducted using a selfdeveloped high vacuum(vacuum degree of 5 Pa)and low-temperature(between-30 and 50℃)simulation platform.It is confirmed that the high-boiling-point reinforcement liquid pre-placed in the drill pipe can be released spontaneously during the drilling process and finally complete the reinforcement of the borehole.The reinforcement effect of the borehole is better when the solute concentration is between0.15 and 0.25 g/mL.展开更多
Carbon fiber reinforced polymer(CFRP)is an advanced material widely used in bridge structures,demonstrating a promising application prospect.CFRP possesses excellent mechanical properties,construction advantages,and d...Carbon fiber reinforced polymer(CFRP)is an advanced material widely used in bridge structures,demonstrating a promising application prospect.CFRP possesses excellent mechanical properties,construction advantages,and durability benefits.Its application in bridge reinforcement can significantly enhance the overall performance of the reinforced bridge,thereby improving the durability and extending the service life of the bridge.Therefore,it is necessary to further explore how CFRP can be effectively applied in bridge reinforcement projects to improve the quality of such projects and ensure the safety of bridges during operation.展开更多
The test results of eight concrete beams reinforced with carbon fiber reinforced polymer （CFRP） sheets subjected to an aggressive environment under a sustained load are presented. The beams are 1 700 mm long with a ...The test results of eight concrete beams reinforced with carbon fiber reinforced polymer （CFRP） sheets subjected to an aggressive environment under a sustained load are presented. The beams are 1 700 mm long with a rectangular cross-section of 120- mm width and 200-mm depth. The beams are precracked with a four-point flexural load, bonded CFRP sheets, and placed into wet-dry saline water（ NaCl） either in an unstressed state or loaded to about 30% or 60% of the initial ultimate load. The individual and coupled effects of wet-dry saline water and sustained bending stresses on the long term behaviour of concrete beams reinforced with the CFRP are investigated. The test results show that the coupled action of wet-dry saline water and sustained bending stresses appears to significantly affect the load capacity and the failure mode of beam strengthened with CFRP, mainly due to the degradation of the bond between CFRP and concrete. However, the stiffness is not affected by the coupled action of wet-dry cycles and a sustained load.展开更多
To improve the wettability of hypereutectic Al−60Si alloy and enhance the mechanical properties of the joints,Al−60Si alloy was joined by ultrasonic soldering with Sn-9Zn solder,and a sound joint with in-situ Si parti...To improve the wettability of hypereutectic Al−60Si alloy and enhance the mechanical properties of the joints,Al−60Si alloy was joined by ultrasonic soldering with Sn-9Zn solder,and a sound joint with in-situ Si particle reinforcement was obtained.The oxide film of Al−60Si alloy at the interface was identified by transmission electron microscopy(TEM)analysis as amorphous Al_(2)O_(3).The oxide of Si particles in the base metal was also alumina.The oxide film of Al−60Si alloy was observed to be removed by ultrasonic vibration instead of holding treatment.Si particle-reinforced joints(35.7 vol.%)were obtained by increasing the ultrasonication time.The maximum shear strength peaked at 99.5 MPa for soldering at 330℃with an ultrasonic vibration time of 50 s.A model of forming of Si particles reinforced joint under the ultrasound was proposed,and ultrasonic vibration was considered to promote the dissolution of Al and migration of Si particles.展开更多
This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary obj...This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary objective is to explore the unknown environments to locate and track targets effectively. To address this problem, we propose a novel Multi-Agent Reinforcement Learning (MARL) method based on Graph Neural Network (GNN). Firstly, a method is introduced for encoding continuous-space multi-UAV problem data into spatial graphs which establish essential relationships among agents, obstacles, and targets. Secondly, a Graph AttenTion network (GAT) model is presented, which focuses exclusively on adjacent nodes, learns attention weights adaptively and allows agents to better process information in dynamic environments. Reward functions are specifically designed to tackle exploration challenges in environments with sparse rewards. By introducing a framework that integrates centralized training and distributed execution, the advancement of models is facilitated. Simulation results show that the proposed method outperforms the existing MARL method in search rate and tracking performance with less collisions. The experiments show that the proposed method can be extended to applications with a larger number of agents, which provides a potential solution to the challenging problem of multi-UAV autonomous tracking in dynamic unknown environments.展开更多
To solve problems of poor security guarantee and insufficient training efficiency in the conventional reinforcement learning methods for decision-making,this study proposes a hybrid framework to combine deep reinforce...To solve problems of poor security guarantee and insufficient training efficiency in the conventional reinforcement learning methods for decision-making,this study proposes a hybrid framework to combine deep reinforcement learning with rule-based decision-making methods.A risk assessment model for lane-change maneuvers considering uncertain predictions of surrounding vehicles is established as a safety filter to improve learning efficiency while correcting dangerous actions for safety enhancement.On this basis,a Risk-fused DDQN is constructed utilizing the model-based risk assessment and supervision mechanism.The proposed reinforcement learning algorithm sets up a separate experience buffer for dangerous trials and punishes such actions,which is shown to improve the sampling efficiency and training outcomes.Compared with conventional DDQN methods,the proposed algorithm improves the convergence value of cumulated reward by 7.6%and 2.2%in the two constructed scenarios in the simulation study and reduces the number of training episodes by 52.2%and 66.8%respectively.The success rate of lane change is improved by 57.3%while the time headway is increased at least by 16.5%in real vehicle tests,which confirms the higher training efficiency,scenario adaptability,and security of the proposed Risk-fused DDQN.展开更多
This study investigates the flexural performance of ultra-high performance concrete(UHPC)in reinforced concrete T-beams,focusing on the effects of interfacial treatments.Three concrete T-beam specimens were fabricated...This study investigates the flexural performance of ultra-high performance concrete(UHPC)in reinforced concrete T-beams,focusing on the effects of interfacial treatments.Three concrete T-beam specimens were fabricated and tested:a control beam(RC-T),a UHPC-reinforced beam with a chiseled interface(UN-C-50F),and a UHPC-reinforced beam featuring both a chiseled interface and anchored steel rebars(UN-CS-50F).The test results indicated that both chiseling and the incorporation of anchored rebars effectively created a synergistic combination between the concrete T-beam and the UHPC reinforcement layer,with the UN-CS-50F exhibiting the highest flexural resistance.The cracking load and ultimate load of UN-CS-50F were 221.5%and 40.8%,respectively,higher than those of the RC-T.Finite element(FE)models were developed to provide further insights into the behavior of the UHPCreinforced T-beams,showing a maximumdeviation of just 8%when validated against experimental data.A parametric analysis varied the height,thickness,andmaterial strength of the UHPC reinforcement layer based on the validated FE model,revealing that increasing the UHPC layer thickness from 30 to 50 mm improved the ultimate resistance by 20%while reducing the UHPC reinforcement height from 440 to 300 mm led to a 10%decrease in bending resistance.The interfacial anchoring rebars significantly reduced crack propagation and enhanced stress redistribution,highlighting the importance of strengthening interfacial bonds and optimizing geometric parameters ofUHPCfor improved T-beam performance.These findings offer valuable insights for the design and retrofitting of UHPC-reinforced bridge girders.展开更多
This review provides a comprehensive overview of natural rubber(NR)composites,focusing on their properties,compounding aspects,and renewable practices involving natural fibre reinforcement.The properties of NR are inf...This review provides a comprehensive overview of natural rubber(NR)composites,focusing on their properties,compounding aspects,and renewable practices involving natural fibre reinforcement.The properties of NR are influenced by the compounding process,which incorporates ingredients such as elastomers,vulcanizing agents,accelerators,activators,and fillers like carbon black and silica.While effective in enhancing properties,these fillers lack biodegradability,prompting the exploration of sustainable alternatives.The potential of natural fibres as renewable reinforcements in NR composites is thoroughly covered in this review,highlighting both their advan-tages,such as improved sustainability,and the challenges they present,such as compatibility with the rubber matrix.Surface treatment methods,including alkali and silane treatments,are also discussed as solutions to improve fibre-matrix adhesion and mitigate these challenges.Additionally,the review highlights the potential of oil palm empty fruit bunch(EFB)fibres as a natural fibre reinforcement.The abundance of EFB fibres and their alignment with sustainable practices make them promising substitutes for conventional fillers,contributing to valuable knowledge and supporting the broader move towards renewable reinforcement to improve sustain-ability without compromising the key properties of rubber composites.展开更多
Granite residual soil (GRS) is a type of weathering soil that can decompose upon contact with water, potentially causing geological hazards. In this study, cement, an alkaline solution, and glass fiber were used to re...Granite residual soil (GRS) is a type of weathering soil that can decompose upon contact with water, potentially causing geological hazards. In this study, cement, an alkaline solution, and glass fiber were used to reinforce GRS. The effects of cement content and SiO_(2)/Na2O ratio of the alkaline solution on the static and dynamic strengths of GRS were discussed. Microscopically, the reinforcement mechanism and coupling effect were examined using X-ray diffraction (XRD), micro-computed tomography (micro-CT), and scanning electron microscopy (SEM). The results indicated that the addition of 2% cement and an alkaline solution with an SiO_(2)/Na2O ratio of 0.5 led to the densest matrix, lowest porosity, and highest static compressive strength, which was 4994 kPa with a dynamic impact resistance of 75.4 kN after adding glass fiber. The compressive strength and dynamic impact resistance were a result of the coupling effect of cement hydration, a pozzolanic reaction of clay minerals in the GRS, and the alkali activation of clay minerals. Excessive cement addition or an excessively high SiO_(2)/Na2O ratio in the alkaline solution can have negative effects, such as the destruction of C-(A)-S-H gels by the alkaline solution and hindering the production of N-A-S-H gels. This can result in damage to the matrix of reinforced GRS, leading to a decrease in both static and dynamic strengths. This study suggests that further research is required to gain a more precise understanding of the effects of this mixture in terms of reducing our carbon footprint and optimizing its properties. The findings indicate that cement and alkaline solution are appropriate for GRS and that the reinforced GRS can be used for high-strength foundation and embankment construction. The study provides an analysis of strategies for mitigating and managing GRS slope failures, as well as enhancing roadbed performance.展开更多
With the change of the main influencing factors such as structural configuration and impact conditions,reinforced concrete slabs exhibit different mechanical behaviors with different failure patterns,and the failure m...With the change of the main influencing factors such as structural configuration and impact conditions,reinforced concrete slabs exhibit different mechanical behaviors with different failure patterns,and the failure modes are transformed.In order to reveal the failure mode and transformation rule of reinforced concrete slabs under impact loads,a dynamic impact response test was carried out using a drop hammer test device.The dynamic data pertaining to the impact force,support reaction force,structural displacement,and reinforcement strain were obtained through the use of digital image correlation technology(DIC),impact force measurement,and strain measurement.The analysis of the ultimate damage state of the reinforced concrete slab identified four distinct types of impact failure modes:local failure by stamping,overall failure by stamping,local-overall coupling failure,and local failure by punching.Additionally,the influence laws of hammerhead shape,hammer height,and reinforcement ratio on the dynamic response and failure mode transformation of the slab were revealed.The results indicate that:(1)The local damage to the slab by the plane hammer is readily apparent,while the overall damage by the spherical hammer is more pronounced.(2)In comparison to the high reinforcement ratio slabs,the overall bending resistance of the low reinforcement ratio slabs is significantly inferior,and the slab back exhibits further cracks.(3)As the hammer height increases,the slab failure mode undergoes a transformation,shifting from local failure by stamping and overall failure by stamping to local-overall coupling failure and local failure by punching.(4)Three failure mode thresholds have been established,and by comparing the peak impact force with the failure thresholds,the failure mode of the slab can be effectively determined.展开更多
Cooperative multi-agent reinforcement learning(MARL)is a key technology for enabling cooperation in complex multi-agent systems.It has achieved remarkable progress in areas such as gaming,autonomous driving,and multi-...Cooperative multi-agent reinforcement learning(MARL)is a key technology for enabling cooperation in complex multi-agent systems.It has achieved remarkable progress in areas such as gaming,autonomous driving,and multi-robot control.Empowering cooperative MARL with multi-task decision-making capabilities is expected to further broaden its application scope.In multi-task scenarios,cooperative MARL algorithms need to address 3 types of multi-task problems:reward-related multi-task,arising from different reward functions;multi-domain multi-task,caused by differences in state and action spaces,state transition functions;and scalability-related multi-task,resulting from the dynamic variation in the number of agents.Most existing studies focus on scalability-related multitask problems.However,with the increasing integration between large language models(LLMs)and multi-agent systems,a growing number of LLM-based multi-agent systems have emerged,enabling more complex multi-task cooperation.This paper provides a comprehensive review of the latest advances in this field.By combining multi-task reinforcement learning with cooperative MARL,we categorize and analyze the 3 major types of multi-task problems under multi-agent settings,offering more fine-grained classifications and summarizing key insights for each.In addition,we summarize commonly used benchmarks and discuss future directions of research in this area,which hold promise for further enhancing the multi-task cooperation capabilities of multi-agent systems and expanding their practical applications in the real world.展开更多
Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision strike.To address the problem of Integrated Evasion and Impact(IEI)decision u...Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision strike.To address the problem of Integrated Evasion and Impact(IEI)decision under multi-constraint conditions,a hierarchical intelligent decision-making method based on Deep Reinforcement Learning(DRL)was proposed.First,an intelligent decision-making framework of“DRL evasion decision”+“impact prediction guidance decision”was established:it takes the impact point deviation correction ability as the constraint and the maximum miss distance as the objective,and effectively solves the problem of poor decisionmaking effect caused by the large IEI decision space.Second,to solve the sparse reward problem faced by evasion decision-making,a hierarchical decision-making method consisting of maneuver timing decision and maneuver duration decision was proposed,and the corresponding Markov Decision Process(MDP)was designed.A detailed simulation experiment was designed to analyze the advantages and computational complexity of the proposed method.Simulation results show that the proposed model has good performance and low computational resource requirement.The minimum miss distance is 21.3 m under the condition of guaranteeing the impact point accuracy,and the single decision-making time is 4.086 ms on an STM32F407 single-chip microcomputer,which has engineering application value.展开更多
To enhance the anti-corrosion performance of TC4 alloy across a wide temperature range for modern aircrafts operating in increasingly harsh environments, the (TiB+TiC) hybrid reinforced TC4 composites were prepared by...To enhance the anti-corrosion performance of TC4 alloy across a wide temperature range for modern aircrafts operating in increasingly harsh environments, the (TiB+TiC) hybrid reinforced TC4 composites were prepared by laser melting deposition (LMD) via the in-situ reaction between B_(4)C reinforcement and molten TC4 alloy. The effect of B_(4)C content (0, 0.5, 1.5, wt%) on the microstructure and room/high-temperature corrosion behaviour of the composites was investigated. Microstructural analysis revealed that the microstructure of the composites was significantly influenced by the B_(4)C content. The composite containing 0.5 wt% B_(4)C exhibited an optimal microstructure characterized by refined grains, equiaxed α-Ti transformed from lath-shaped α-Ti, well-distributed (TiB+TiC) phases with a proper amount and reduced pore/dislocation defects. This composite also demonstrated the best corrosion resistance at both room temperature (25 ℃) and high temperature (800 ℃), which was primarily attributed to its comprehensive advantages including a favorable microstructure, a uniform dispersion of thermally stable (TiB+TiC) phases and a stable passivation film.展开更多
Grouting has been the most effective approach to mitigate water inrush disasters in underground engineering due to its ability to plug groundwater and enhance rock strength.Nevertheless,there is a lack of potent numer...Grouting has been the most effective approach to mitigate water inrush disasters in underground engineering due to its ability to plug groundwater and enhance rock strength.Nevertheless,there is a lack of potent numerical tools for assessing the grouting effectiveness in water-rich fractured strata.In this study,the hydro-mechanical coupled discontinuous deformation analysis(HM-DDA)is inaugurally extended to simulate the grouting process in a water-rich discrete fracture network(DFN),including the slurry migration,fracture dilation,water plugging in a seepage field,and joint reinforcement after coagulation.To validate the capabilities of the developed method,several numerical examples are conducted incorporating the Newtonian fluid and Bingham slurry.The simulation results closely align with the analytical solutions.Additionally,a set of compression tests is conducted on the fresh and grouted rock specimens to verify the reinforcement method and calibrate the rational properties of reinforced joints.An engineering-scale model based on a real water inrush case of the Yonglian tunnel in a water-rich fractured zone has been established.The model demonstrates the effectiveness of grouting reinforcement in mitigating water inrush disaster.The results indicate that increased grouting pressure greatly affects the regulation of water outflow from the tunnel face and the prevention of rock detachment face after excavation.展开更多
基金funded by Hung Yen University of Technology and Education under grand number UTEHY.L.2025.62.
文摘Unmanned Aerial Vehicles(UAVs)have become integral components in smart city infrastructures,supporting applications such as emergency response,surveillance,and data collection.However,the high mobility and dynamic topology of Flying Ad Hoc Networks(FANETs)present significant challenges for maintaining reliable,low-latency communication.Conventional geographic routing protocols often struggle in situations where link quality varies and mobility patterns are unpredictable.To overcome these limitations,this paper proposes an improved routing protocol based on reinforcement learning.This new approach integrates Q-learning with mechanisms that are both link-aware and mobility-aware.The proposed method optimizes the selection of relay nodes by using an adaptive reward function that takes into account energy consumption,delay,and link quality.Additionally,a Kalman filter is integrated to predict UAV mobility,improving the stability of communication links under dynamic network conditions.Simulation experiments were conducted using realistic scenarios,varying the number of UAVs to assess scalability.An analysis was conducted on key performance metrics,including the packet delivery ratio,end-to-end delay,and total energy consumption.The results demonstrate that the proposed approach significantly improves the packet delivery ratio by 12%–15%and reduces delay by up to 25.5%when compared to conventional GEO and QGEO protocols.However,this improvement comes at the cost of higher energy consumption due to additional computations and control overhead.Despite this trade-off,the proposed solution ensures reliable and efficient communication,making it well-suited for large-scale UAV networks operating in complex urban environments.
基金funded by the Beijing Engineering Research Center of Electric Rail Transportation.
文摘Effective partitioning is crucial for enabling parallel restoration of power systems after blackouts.This paper proposes a novel partitioning method based on deep reinforcement learning.First,the partitioning decision process is formulated as a Markov decision process(MDP)model to maximize the modularity.Corresponding key partitioning constraints on parallel restoration are considered.Second,based on the partitioning objective and constraints,the reward function of the partitioning MDP model is set by adopting a relative deviation normalization scheme to reduce mutual interference between the reward and penalty in the reward function.The soft bonus scaling mechanism is introduced to mitigate overestimation caused by abrupt jumps in the reward.Then,the deep Q network method is applied to solve the partitioning MDP model and generate partitioning schemes.Two experience replay buffers are employed to speed up the training process of the method.Finally,case studies on the IEEE 39-bus test system demonstrate that the proposed method can generate a high-modularity partitioning result that meets all key partitioning constraints,thereby improving the parallelism and reliability of the restoration process.Moreover,simulation results demonstrate that an appropriate discount factor is crucial for ensuring both the convergence speed and the stability of the partitioning training.
基金supported by Key Science and Technology Program of Henan Province,China(Grant Nos.242102210147,242102210027)Fujian Province Young and Middle aged Teacher Education Research Project(Science and Technology Category)(No.JZ240101)(Corresponding author:Dong Yuan).
文摘Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.
文摘At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown and complex environments,this paper proposes an Attention-Enhanced Dueling Deep Q-Network(ADDueling DQN),which integrates a multi-head attention mechanism and a prioritized experience replay strategy into a Dueling-DQN reinforcement learning framework.A multi-objective reward function,centered on energy efficiency,is designed to comprehensively consider path length,terrain slope,motion smoothness,and obstacle avoidance,enabling optimal low-energy trajectory generation in 3D space from the source.The incorporation of a multihead attention mechanism allows the model to dynamically focus on energy-critical state features—such as slope gradients and obstacle density—thereby significantly improving its ability to recognize and avoid energy-intensive paths.Additionally,the prioritized experience replay mechanism accelerates learning from key decision-making experiences,suppressing inefficient exploration and guiding the policy toward low-energy solutions more rapidly.The effectiveness of the proposed path planning algorithm is validated through simulation experiments conducted in multiple off-road scenarios.Results demonstrate that AD-Dueling DQN consistently achieves the lowest average energy consumption across all tested environments.Moreover,the proposed method exhibits faster convergence and greater training stability compared to baseline algorithms,highlighting its global optimization capability under energy-aware objectives in complex terrains.This study offers an efficient and scalable intelligent control strategy for the development of energy-conscious autonomous navigation systems.
基金funding from the National Key Research and Development Program of China(No.2018YFE0110000)the National Natural Science Foundation of China(No.11274259,No.11574258)the Science and Technology Commission Foundation of Shanghai(21DZ1205500)in support of the present research.
文摘While reinforcement learning-based underwater acoustic adaptive modulation shows promise for enabling environment-adaptive communication as supported by extensive simulation-based research,its practical performance remains underexplored in field investigations.To evaluate the practical applicability of this emerging technique in adverse shallow sea channels,a field experiment was conducted using three communication modes:orthogonal frequency division multiplexing(OFDM),M-ary frequency-shift keying(MFSK),and direct sequence spread spectrum(DSSS)for reinforcement learning-driven adaptive modulation.Specifically,a Q-learning method is used to select the optimal modulation mode according to the channel quality quantified by signal-to-noise ratio,multipath spread length,and Doppler frequency offset.Experimental results demonstrate that the reinforcement learning-based adaptive modulation scheme outperformed fixed threshold detection in terms of total throughput and average bit error rate,surpassing conventional adaptive modulation strategies.
基金supported by the National Natural Science Foundation of China(Grant Nos.51978672 and 52308335)the Natural Science Funding of Hunan Province(Grant No.2023JJ41054)the Natural Science Research Project of Anhui Educational Committee(Grant No.2023AH051170)。
文摘Understanding the reinforcement effect of the newly developed prestressed reinforcement components(PRCs)(a system composed of prestressed steel bars(PSBs),protective sleeves,lateral pressure plates(LPPs),and anchoring elements)is technically significant for the rational design of prestressed subgrade.A three-dimensional finite element model was established and verified based on a novel static model test and utilized to systematically analyze the influence of prestress levels and reinforcement modes on the reinforcement effect of the subgrade.The results show that the PRCs provide additional confining pressure to the subgrade through the diffusion effect of the prestress,which can therefore effectively improve the service performance of the subgrade.Compared to the unreinforced conventional subgrades,the settlements of prestressreinforced subgrades are reduced.The settlement attenuation rate(Rs)near the LPPs is larger than that at the subgrade center,and increasing the prestress positively contributes to the stability of the subgrade structure.In the multi-row reinforcement mode,the reinforcement effect of PRCs can extend from the reinforced area to the unreinforced area.In addition,as the horizontal distance from the LPPs increases,the additional confining pressure converted by the PSBs and LPPs gradually diminishes when spreading to the core load bearing area of the subgrade,resulting in a decrease in the Rs.Under the singlerow reinforcement mode,PRCs can be strategically arranged according to the local areas where subgrade defects readily occurred or observed,to obtain the desired reinforcement effect.Moreover,excessive prestress should not be applied near the subgrade shoulder line to avoid the shear failure of the subgrade shoulder.PRCs can be flexibly used for preventing and treating various subgrade defects of newly constructed or existing railway lines,achieving targeted and classified prevention,and effectively improving the bearing performance and deformation resistance of the subgrade.The research results are instructive for further elucidating the prestress reinforcement effect of PRCs on railway subgrades.
基金National Natural Science Foundation of China (Nos.U2013603,51827901,and 52403383)Program for Guangdong Introducing Innovative and Entrepreneurial Teams (No.2019ZT08G315)+1 种基金Institute of New Energy and Low-Carbon Technology (Sichuan University)State Key Laboratory of Coal Mine Disaster Dynamics and Control of Chongqing University。
文摘Lunar core samples are the key materials for accurately assessing and developing lunar resources.However,the difficulty of maintaining borehole stability in the lunar coring process limits the depth of lunar coring.Here,a strategy of using a reinforcement fluid that undergoes a phase transition spontaneously in a vacuum environment to reinforce the borehole is proposed.Based on this strategy,a reinforcement liquid suitable for a wide temperature range and a high vacuum environment was developed.A feasibility study on reinforcing the borehole with the reinforcement liquid was carried out,and it is found that the cohesion of the simulated lunar soil can be increased from 2 to 800 kPa after using the reinforcement liquid.Further,a series of coring experiments are conducted using a selfdeveloped high vacuum(vacuum degree of 5 Pa)and low-temperature(between-30 and 50℃)simulation platform.It is confirmed that the high-boiling-point reinforcement liquid pre-placed in the drill pipe can be released spontaneously during the drilling process and finally complete the reinforcement of the borehole.The reinforcement effect of the borehole is better when the solute concentration is between0.15 and 0.25 g/mL.
文摘Carbon fiber reinforced polymer(CFRP)is an advanced material widely used in bridge structures,demonstrating a promising application prospect.CFRP possesses excellent mechanical properties,construction advantages,and durability benefits.Its application in bridge reinforcement can significantly enhance the overall performance of the reinforced bridge,thereby improving the durability and extending the service life of the bridge.Therefore,it is necessary to further explore how CFRP can be effectively applied in bridge reinforcement projects to improve the quality of such projects and ensure the safety of bridges during operation.
基金The National Natural Science Foundation of China(No.50608013)Special Prophase Project on Basic Research of the National Department of Science and Technology(No.2004CCA04100)
文摘The test results of eight concrete beams reinforced with carbon fiber reinforced polymer （CFRP） sheets subjected to an aggressive environment under a sustained load are presented. The beams are 1 700 mm long with a rectangular cross-section of 120- mm width and 200-mm depth. The beams are precracked with a four-point flexural load, bonded CFRP sheets, and placed into wet-dry saline water（ NaCl） either in an unstressed state or loaded to about 30% or 60% of the initial ultimate load. The individual and coupled effects of wet-dry saline water and sustained bending stresses on the long term behaviour of concrete beams reinforced with the CFRP are investigated. The test results show that the coupled action of wet-dry saline water and sustained bending stresses appears to significantly affect the load capacity and the failure mode of beam strengthened with CFRP, mainly due to the degradation of the bond between CFRP and concrete. However, the stiffness is not affected by the coupled action of wet-dry cycles and a sustained load.
基金financial support from the National Natural Science Foundation of China(Nos.52275385,U2167216)Sichuan Province Science and Technology Support Program,China(No.2022YFG0086).
文摘To improve the wettability of hypereutectic Al−60Si alloy and enhance the mechanical properties of the joints,Al−60Si alloy was joined by ultrasonic soldering with Sn-9Zn solder,and a sound joint with in-situ Si particle reinforcement was obtained.The oxide film of Al−60Si alloy at the interface was identified by transmission electron microscopy(TEM)analysis as amorphous Al_(2)O_(3).The oxide of Si particles in the base metal was also alumina.The oxide film of Al−60Si alloy was observed to be removed by ultrasonic vibration instead of holding treatment.Si particle-reinforced joints(35.7 vol.%)were obtained by increasing the ultrasonication time.The maximum shear strength peaked at 99.5 MPa for soldering at 330℃with an ultrasonic vibration time of 50 s.A model of forming of Si particles reinforced joint under the ultrasound was proposed,and ultrasonic vibration was considered to promote the dissolution of Al and migration of Si particles.
基金supported by the National Natural Science Foundation of China(Nos.12272104,U22B2013).
文摘This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary objective is to explore the unknown environments to locate and track targets effectively. To address this problem, we propose a novel Multi-Agent Reinforcement Learning (MARL) method based on Graph Neural Network (GNN). Firstly, a method is introduced for encoding continuous-space multi-UAV problem data into spatial graphs which establish essential relationships among agents, obstacles, and targets. Secondly, a Graph AttenTion network (GAT) model is presented, which focuses exclusively on adjacent nodes, learns attention weights adaptively and allows agents to better process information in dynamic environments. Reward functions are specifically designed to tackle exploration challenges in environments with sparse rewards. By introducing a framework that integrates centralized training and distributed execution, the advancement of models is facilitated. Simulation results show that the proposed method outperforms the existing MARL method in search rate and tracking performance with less collisions. The experiments show that the proposed method can be extended to applications with a larger number of agents, which provides a potential solution to the challenging problem of multi-UAV autonomous tracking in dynamic unknown environments.
基金Supported by National Key Research and Development Program of China(Grant No.2022YFE0117100)National Science Foundation of China(Grant No.52102468,52325212)Fundamental Research Funds for the Central Universities。
文摘To solve problems of poor security guarantee and insufficient training efficiency in the conventional reinforcement learning methods for decision-making,this study proposes a hybrid framework to combine deep reinforcement learning with rule-based decision-making methods.A risk assessment model for lane-change maneuvers considering uncertain predictions of surrounding vehicles is established as a safety filter to improve learning efficiency while correcting dangerous actions for safety enhancement.On this basis,a Risk-fused DDQN is constructed utilizing the model-based risk assessment and supervision mechanism.The proposed reinforcement learning algorithm sets up a separate experience buffer for dangerous trials and punishes such actions,which is shown to improve the sampling efficiency and training outcomes.Compared with conventional DDQN methods,the proposed algorithm improves the convergence value of cumulated reward by 7.6%and 2.2%in the two constructed scenarios in the simulation study and reduces the number of training episodes by 52.2%and 66.8%respectively.The success rate of lane change is improved by 57.3%while the time headway is increased at least by 16.5%in real vehicle tests,which confirms the higher training efficiency,scenario adaptability,and security of the proposed Risk-fused DDQN.
基金The National Natural Science Foundation of China(Grant#52278161)the Science and Technology Project of Guangzhou(Grant#2024A04J9888)the Guangdong Basic and Applied Basic Research Foundation(Grant#2023A1515010535).
文摘This study investigates the flexural performance of ultra-high performance concrete(UHPC)in reinforced concrete T-beams,focusing on the effects of interfacial treatments.Three concrete T-beam specimens were fabricated and tested:a control beam(RC-T),a UHPC-reinforced beam with a chiseled interface(UN-C-50F),and a UHPC-reinforced beam featuring both a chiseled interface and anchored steel rebars(UN-CS-50F).The test results indicated that both chiseling and the incorporation of anchored rebars effectively created a synergistic combination between the concrete T-beam and the UHPC reinforcement layer,with the UN-CS-50F exhibiting the highest flexural resistance.The cracking load and ultimate load of UN-CS-50F were 221.5%and 40.8%,respectively,higher than those of the RC-T.Finite element(FE)models were developed to provide further insights into the behavior of the UHPCreinforced T-beams,showing a maximumdeviation of just 8%when validated against experimental data.A parametric analysis varied the height,thickness,andmaterial strength of the UHPC reinforcement layer based on the validated FE model,revealing that increasing the UHPC layer thickness from 30 to 50 mm improved the ultimate resistance by 20%while reducing the UHPC reinforcement height from 440 to 300 mm led to a 10%decrease in bending resistance.The interfacial anchoring rebars significantly reduced crack propagation and enhanced stress redistribution,highlighting the importance of strengthening interfacial bonds and optimizing geometric parameters ofUHPCfor improved T-beam performance.These findings offer valuable insights for the design and retrofitting of UHPC-reinforced bridge girders.
基金funded under the Collaborative Research Initiative Grant Scheme(C-RIGS),grant number C-RIGS24-016-0022 from IIUM.
文摘This review provides a comprehensive overview of natural rubber(NR)composites,focusing on their properties,compounding aspects,and renewable practices involving natural fibre reinforcement.The properties of NR are influenced by the compounding process,which incorporates ingredients such as elastomers,vulcanizing agents,accelerators,activators,and fillers like carbon black and silica.While effective in enhancing properties,these fillers lack biodegradability,prompting the exploration of sustainable alternatives.The potential of natural fibres as renewable reinforcements in NR composites is thoroughly covered in this review,highlighting both their advan-tages,such as improved sustainability,and the challenges they present,such as compatibility with the rubber matrix.Surface treatment methods,including alkali and silane treatments,are also discussed as solutions to improve fibre-matrix adhesion and mitigate these challenges.Additionally,the review highlights the potential of oil palm empty fruit bunch(EFB)fibres as a natural fibre reinforcement.The abundance of EFB fibres and their alignment with sustainable practices make them promising substitutes for conventional fillers,contributing to valuable knowledge and supporting the broader move towards renewable reinforcement to improve sustain-ability without compromising the key properties of rubber composites.
基金the support provided by the National Natural Science Foundation of China(Grant Nos.52278336 and 42302032)Guangdong Basic and Applied Research Foundation(Grant Nos.2023B1515020061).
文摘Granite residual soil (GRS) is a type of weathering soil that can decompose upon contact with water, potentially causing geological hazards. In this study, cement, an alkaline solution, and glass fiber were used to reinforce GRS. The effects of cement content and SiO_(2)/Na2O ratio of the alkaline solution on the static and dynamic strengths of GRS were discussed. Microscopically, the reinforcement mechanism and coupling effect were examined using X-ray diffraction (XRD), micro-computed tomography (micro-CT), and scanning electron microscopy (SEM). The results indicated that the addition of 2% cement and an alkaline solution with an SiO_(2)/Na2O ratio of 0.5 led to the densest matrix, lowest porosity, and highest static compressive strength, which was 4994 kPa with a dynamic impact resistance of 75.4 kN after adding glass fiber. The compressive strength and dynamic impact resistance were a result of the coupling effect of cement hydration, a pozzolanic reaction of clay minerals in the GRS, and the alkali activation of clay minerals. Excessive cement addition or an excessively high SiO_(2)/Na2O ratio in the alkaline solution can have negative effects, such as the destruction of C-(A)-S-H gels by the alkaline solution and hindering the production of N-A-S-H gels. This can result in damage to the matrix of reinforced GRS, leading to a decrease in both static and dynamic strengths. This study suggests that further research is required to gain a more precise understanding of the effects of this mixture in terms of reducing our carbon footprint and optimizing its properties. The findings indicate that cement and alkaline solution are appropriate for GRS and that the reinforced GRS can be used for high-strength foundation and embankment construction. The study provides an analysis of strategies for mitigating and managing GRS slope failures, as well as enhancing roadbed performance.
基金Supported by the National Natural Science Foundation of China(Grant No.52078283)Shandong Provincial Natural Science Foundation(Project No.ZR2024MA094)。
文摘With the change of the main influencing factors such as structural configuration and impact conditions,reinforced concrete slabs exhibit different mechanical behaviors with different failure patterns,and the failure modes are transformed.In order to reveal the failure mode and transformation rule of reinforced concrete slabs under impact loads,a dynamic impact response test was carried out using a drop hammer test device.The dynamic data pertaining to the impact force,support reaction force,structural displacement,and reinforcement strain were obtained through the use of digital image correlation technology(DIC),impact force measurement,and strain measurement.The analysis of the ultimate damage state of the reinforced concrete slab identified four distinct types of impact failure modes:local failure by stamping,overall failure by stamping,local-overall coupling failure,and local failure by punching.Additionally,the influence laws of hammerhead shape,hammer height,and reinforcement ratio on the dynamic response and failure mode transformation of the slab were revealed.The results indicate that:(1)The local damage to the slab by the plane hammer is readily apparent,while the overall damage by the spherical hammer is more pronounced.(2)In comparison to the high reinforcement ratio slabs,the overall bending resistance of the low reinforcement ratio slabs is significantly inferior,and the slab back exhibits further cracks.(3)As the hammer height increases,the slab failure mode undergoes a transformation,shifting from local failure by stamping and overall failure by stamping to local-overall coupling failure and local failure by punching.(4)Three failure mode thresholds have been established,and by comparing the peak impact force with the failure thresholds,the failure mode of the slab can be effectively determined.
基金The National Natural Science Foundation of China(62136008,62293541)The Beijing Natural Science Foundation(4232056)The Beijing Nova Program(20240484514).
文摘Cooperative multi-agent reinforcement learning(MARL)is a key technology for enabling cooperation in complex multi-agent systems.It has achieved remarkable progress in areas such as gaming,autonomous driving,and multi-robot control.Empowering cooperative MARL with multi-task decision-making capabilities is expected to further broaden its application scope.In multi-task scenarios,cooperative MARL algorithms need to address 3 types of multi-task problems:reward-related multi-task,arising from different reward functions;multi-domain multi-task,caused by differences in state and action spaces,state transition functions;and scalability-related multi-task,resulting from the dynamic variation in the number of agents.Most existing studies focus on scalability-related multitask problems.However,with the increasing integration between large language models(LLMs)and multi-agent systems,a growing number of LLM-based multi-agent systems have emerged,enabling more complex multi-task cooperation.This paper provides a comprehensive review of the latest advances in this field.By combining multi-task reinforcement learning with cooperative MARL,we categorize and analyze the 3 major types of multi-task problems under multi-agent settings,offering more fine-grained classifications and summarizing key insights for each.In addition,we summarize commonly used benchmarks and discuss future directions of research in this area,which hold promise for further enhancing the multi-task cooperation capabilities of multi-agent systems and expanding their practical applications in the real world.
基金co-supported by the National Natural Science Foundation of China(No.62103432)the China Postdoctoral Science Foundation(No.284881)the Young Talent fund of University Association for Science and Technology in Shaanxi,China(No.20210108)。
文摘Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision strike.To address the problem of Integrated Evasion and Impact(IEI)decision under multi-constraint conditions,a hierarchical intelligent decision-making method based on Deep Reinforcement Learning(DRL)was proposed.First,an intelligent decision-making framework of“DRL evasion decision”+“impact prediction guidance decision”was established:it takes the impact point deviation correction ability as the constraint and the maximum miss distance as the objective,and effectively solves the problem of poor decisionmaking effect caused by the large IEI decision space.Second,to solve the sparse reward problem faced by evasion decision-making,a hierarchical decision-making method consisting of maneuver timing decision and maneuver duration decision was proposed,and the corresponding Markov Decision Process(MDP)was designed.A detailed simulation experiment was designed to analyze the advantages and computational complexity of the proposed method.Simulation results show that the proposed model has good performance and low computational resource requirement.The minimum miss distance is 21.3 m under the condition of guaranteeing the impact point accuracy,and the single decision-making time is 4.086 ms on an STM32F407 single-chip microcomputer,which has engineering application value.
基金supported by the Tianjin Municipal Natural Science Foundation(No.23JCYBJC00040)the National Nat-ural Science Foundation of China(No.52175369)the Tian-jin Research Innovation Project for Postgraduate Students(No.2022SKY134).
文摘To enhance the anti-corrosion performance of TC4 alloy across a wide temperature range for modern aircrafts operating in increasingly harsh environments, the (TiB+TiC) hybrid reinforced TC4 composites were prepared by laser melting deposition (LMD) via the in-situ reaction between B_(4)C reinforcement and molten TC4 alloy. The effect of B_(4)C content (0, 0.5, 1.5, wt%) on the microstructure and room/high-temperature corrosion behaviour of the composites was investigated. Microstructural analysis revealed that the microstructure of the composites was significantly influenced by the B_(4)C content. The composite containing 0.5 wt% B_(4)C exhibited an optimal microstructure characterized by refined grains, equiaxed α-Ti transformed from lath-shaped α-Ti, well-distributed (TiB+TiC) phases with a proper amount and reduced pore/dislocation defects. This composite also demonstrated the best corrosion resistance at both room temperature (25 ℃) and high temperature (800 ℃), which was primarily attributed to its comprehensive advantages including a favorable microstructure, a uniform dispersion of thermally stable (TiB+TiC) phases and a stable passivation film.
基金supported by the China Scholarship Council(CSC,Grant No.202108050072)JSPS KAKENHI(Grant No.JP19KK0121)。
文摘Grouting has been the most effective approach to mitigate water inrush disasters in underground engineering due to its ability to plug groundwater and enhance rock strength.Nevertheless,there is a lack of potent numerical tools for assessing the grouting effectiveness in water-rich fractured strata.In this study,the hydro-mechanical coupled discontinuous deformation analysis(HM-DDA)is inaugurally extended to simulate the grouting process in a water-rich discrete fracture network(DFN),including the slurry migration,fracture dilation,water plugging in a seepage field,and joint reinforcement after coagulation.To validate the capabilities of the developed method,several numerical examples are conducted incorporating the Newtonian fluid and Bingham slurry.The simulation results closely align with the analytical solutions.Additionally,a set of compression tests is conducted on the fresh and grouted rock specimens to verify the reinforcement method and calibrate the rational properties of reinforced joints.An engineering-scale model based on a real water inrush case of the Yonglian tunnel in a water-rich fractured zone has been established.The model demonstrates the effectiveness of grouting reinforcement in mitigating water inrush disaster.The results indicate that increased grouting pressure greatly affects the regulation of water outflow from the tunnel face and the prevention of rock detachment face after excavation.
