Assessment of Deviation in Quality of Steel Reinforcing Bars Used in Some Building Sites in Cameroon

The present work evaluated the deviations in the quality of steel reinforcing bars in terms of markings, diameter, yield strength and ductility in order to facilitate the drawing up of a yield strength value for the Cameroon National Annex to Eurocode 2. The methodology of the work started with the collection of steel samples from various active building project sites in four different towns viz: Bamenda, Douala, Maroua and Yaoundé and testing their tensile strength and elongation using a Universal Testing Machine and also carrying out the bending test. Results show that bars without marked manufacturer’s name fell all the tests. Other results show that 52% of all the steel had yield stresses below 400 Mpa and the highest deviation in the yield strengths was 22.50%. The study recommends that properly marked grade 500 steel bars should be adopted in the Cameroon national annex to Eurocode 2.

Influence of Coupled Chemo-Mechanical Process on Corrosion Characteristics in Reinforcing Bars

We studied the corrosion characteristics of reinforcing bars in concrete under different corrosion conditions. The area-box （AB） value was used to classify the shape of pitting corrosion morphology in meso-scale, and fractographs of reinforcing bars with different corrosion morphology were discussed in micro- and macro-scales. The results show that the existence of the tensile stress affects the corrosion characteristics of reinforcing bars. The pitting morphology and fractograph of reinforcing bars exhibit a statistical fractal feature. The linear regression model fits the relationship between fractal dimensions of corrosion morphology and fractal dimension of fractograph fairly well. Using fractal dimension as the characterization parameter can not only reflect the characteristics of pitting corrosion morphology in reinforcing bars, but also reveal the fracture feature of corroded reinforcing bars.

A Study on the Bond Strength between High Performance Concrete and Reinforcing Bar

As a preliminary study for the erection of floating structures using high performance concrete, this paper examines the bond characteristics between concrete and the reinforcing bar. Since the floating structure is constructed in aquatic environment, corrosion of the reinforcing steel is likely to develop more prematurely than in onshore structure in case of concrete cracking. A solution to this corrosion problem could use FRP rebar instead of steel reinforcement. To that goal, an experimental study is conducted on the concrete-FRP bond strength to verify if such FRP rebar develops performance comparable to the conventional steel rebar. A series of tests are performed considering the bond length of ordinary steel rebar and G-FRP rebar as test variable with respect to the strength of concrete, and the results are presented.

Comparative Studies of Steel, Bamboo and Rattan as Reinforcing Bars in Concrete: Tensile and Flexural Characteristics

This study comparatively evaluated the flexural performance and deformation characteristics of concrete elements reinforced with bamboo (Bambusa vulgaris), rattan (Calamuc deerratus) and the twisted steel rebars. The yield strength (YS), ultimate tensile strength (UTS) and the elongation of 50 specimens of the three materials were determined using a universal testing machine. Three beams of concrete strength 20 N/mm2 at age 28 days were separately reinforced with bamboo, rattan and steel bars of same percentage, while the stirrups were essentially mild steel bars. The beams were subjected to centre-point flexural loading according to BS 1881 to evaluate the flexural behaviour. The YS of bamboo and rattan bars were 13% and 45% of that of steel respectively, while their UTS were 16% and 62% of that of steel in the same order. The elongation of bamboo, rattan and steel were 7.42%, 10% and 14.7% respectively. The natural rebars were less than the 12% minimum requirement of BS 4449. The load-deflection plots of bamboo and steel RC beams were quadratic, while rattan RC beams had curvilinear trend. The stiffness of bamboo RC beams (BB) and rattan RC beams (RB) were 32% and 13.5% of the stiffness of steel RC beams (SB). The post-first crack residual flexural strength was 41% for BB and SB, while RB was 25%. Moreover, the moment capacities of BB and RB corresponded to 51% and 21% respectively of the capacity of steel RC beams. The remarkable gap between the flexural capacities of the natural rebars and that of steel can be traced not only to the tensile strength but also the weak bonding at the bar-concrete interface. It can be concluded that the bamboo bars are suitable rebars for non-load bearing and lightweight RC flexural structures, while more pre-strengthening treatment is required more importantly for rattan for improved interfacial bonding and load-carrying capacity.

Experimental Study on the Degradation of Bonding Behavior between Reinforcing Bars and Concrete after Corrosion and Fatigue Damage

In marine environments,the durability of reinforced concrete structures such as bridges,which suffer from the coupled effects of corrosion and fatigue damage,is significantly reduced.Fatigue loading can result in severe dete-rioration of the bonds between reinforcing steel bars and the surrounding concrete,particularly when reinforcing bars are corroded.Uniaxial tension testing was conducted under static loading and fatigue loading conditions to investigate the bonding characteristics between corroded reinforcing bars and concrete.An electrolyte corrosion technique was used to accelerate steel corrosion.The results show that the bond strength was reduced under fati-gue loading,although the concrete did not crack.Therefore,fatigue loading has negative effects on the bond strength between corroded steel bars and concrete.The effects of corrosion cracking on bond strength become more pronounced after corrosion cracking appears along the main reinforcing bars.When the average width of cracking along main reinforcing bars exceeds 3 mm,the bonding properties deteriorate rapidly based on the effects of corrosion cracking,whereas fatigue loading exhibits no additional effects on bond strength.

Analysis model for damage of reinforced bars in RC beams under contact explosion

The load-bearing capacity of reinforced concrete(RC) beams primarily relies on internal reinforced bars.However, limited research has been conducted on the dynamic response of these bars. To address this gap, this study has established an analytical model using dimensional analysis for calculating the deformation of reinforced bars within RC beams subjected to contact explosion. Comparison with experimental data reveals that the model has a relative error of 5.22%, effectively reflecting the deformation of reinforced bars. Additionally, based on this model, the study found that while concrete does influence the deformation of reinforced bars, this influence can be disregarded in comparison to the material properties of the bars themselves. The findings of this study have implications for calculating the residual load-bearing capacity of damaged RC beams, evaluating the extent of damage to RC beams after blast loading, and providing guidance for the blast-resistant design of RC structures.

Elliptical encirclement control capable of reinforcing performances for UAVs around a dynamic target

Most researches associated with target encircling control are focused on moving along a circular orbit under an ideal environment free from external disturbances.However,elliptical encirclement with a time-varying observation radius,may permit a more flexible and high-efficacy enclosing solution,whilst the non-orthogonal property between axial and tangential speed components,non-ignorable environmental perturbations,and strict assignment requirements empower elliptical encircling control to be more challenging,and the relevant investigations are still open.Following this line,an appointed-time elliptical encircling control rule capable of reinforcing circumnavigation performances is developed to enable Unmanned Aerial Vehicles(UAVs)to move along a specified elliptical path within a predetermined reaching time.The remarkable merits of the designed strategy are that the relative distance controlling error can be guaranteed to evolve within specified regions with a designer-specified convergence behavior.Meanwhile,wind perturbations can be online counteracted based on an unknown system dynamics estimator(USDE)with only one regulating parameter and high computational efficiency.Lyapunov tool demonstrates that all involved error variables are ultimately limited,and simulations are implemented to confirm the usability of the suggested control algorithm.

Experimental Study of the Anti-Corrosion Performance of Montmorillonite K-10 on Rebar in HCl Environment: Application in Mortar for the Rehabilitation of Reinforced Concrete Degraded by Corrosion

This work first investigates the corrosion-inhibiting behavior of montmorillonite K-10 on reinforcing steel. The corrosion-inhibiting power of the clay (Montmorillonite) is determined in a medium HCl (C = 1N) using free corrosion potential monitoring, Tafel potentiodynamic polarization curves and electrochemical impedance spectroscopy. The results of this study showed a satisfactory corrosion-inhibiting efficiency of around 72.665% for the optimum content of 1%. This is due to the presence of a stable oxide layer that protects the metal against corrosion. To validate the concept of montmorillonite as a corrosion inhibitor in repair mortar, we now turn to the influence of montmorillonite on the mechanical properties of mortars in the hardened state. In this part, montmorillonite K-10 is added to the mortar by partial substitution of the cement by 5% and 10% of the cement mass. The aim of this study is to ensure that the addition of this clay to the mortar composition will not have a negative effect on its compressive and flexural strengths. The results of the compression and flexural tests showed that the presence of montmorillonite in the mortar improved flexural and compressive strengths for the different compositions studied.

Field implementation of enzyme-induced carbonate precipitation technology for reinforcing a bedding layer beneath an underground cable duct

A suitable bearing capacity of foundation is critical for the safety of civil structures.Sometimes foundation reinforcement is necessary and an effective and environmentally friendly method would be the preferred choice.In this study,the potential application of enzyme-induced carbonate precipitation(EICP)was investigated for reinforcing a 0.6 m bedding layer on top of clay to improve the bearing capacity of the foundation underneath an underground cable duct.Laboratory experiments were conducted to determine the optimal operational parameters for the extraction of crude urease liquid and optimal grain size range of sea sands to be used to construct the bedding layer.Field tests were planned based on orthogonal experimental design to study the factors that would significantly affect the biocementation effect on site.The dynamic deformation modulus,calcium carbonate content and longterm ground stress variations were used to evaluate the bio-cementation effect and the long-term performance of the EICP-treated bedding layer.The laboratory test results showed that the optimal duration for the extraction of crude urease liquid is 1 h and the optimal usage of soybean husk powder in urease extraction solution is 100 g/L.The calcium carbonate production rate decreases significantly when the concentration of cementation solution exceeds 0.5 mol/L.The results of site trial showed that the number of EICP treatments has the most significant impact on the effectiveness of EICP treatment and the highest dynamic deformation modulus(Evd)of EICP-treated bedding layer reached 50.55 MPa.The area with better bio-cementation effect was found to take higher ground stress which validates that the EICP treatment could improve the bearing capacity of foundation by reinforcing the bedding layer.The field trial described and the analysis introduced in this paper can provide a practical basis for applying EICP technology to the reinforcement of bedding layer in poor ground conditions.

The Clinical Observation of 80 Patients with Diseases of Lumbar Vertebrae Treated by Reinforcing and Reducing Methods of Mani

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The dynamic response and damage models of rebar reinforced polymer slabs subjected to contact and near-field explosions

Non aqueous reactive polymer materials produced by the reaction of isocyanate and polyol have been widely used in infrastructure construction,which may be subjected to explosion loads during complex service conditions.The blast response of composite materials is a crucial aspect for applications in engineering structures potentially subjected to extreme loadings.In this work,damage caused to rebar reinforced polymer slabs by surface explosive charges was studied experimentally and numerically.A total of 6 field tests were carried out to investigate the performances of the failure modes of rebar reinforced polymer slabs under contact and near-field explosions.The influence of explosive quantity(10-40 g)and stand-off distances(0-20 cm)at the damage modes were studied.The results show that the failure modes of rebar reinforced polymer slabs under near-field explosion mainly were bending and surface spalling,while under the impact of contact explosion,the failure modes were craters of the top surface,spalling of the bottom surface,and middle perforation.Furthermore,a detailed fully coupled model was developed and validated with the test data.The influences of explosive quantity and slab thickness on rebar reinforced polymer slabs under contact explosion were studied.Based on this,the calculation formula between breach diameter,explosive quantity,and slab thickness is fitted.

Comparative Evaluation of the Chemical Composition and Physical Properties of Reinforced Concrete Steel Bars Used in Construction in Senegal

This article presents, the study of a comparative evaluation of the chemical composition and physical properties, linear mass deviations, of four (04) types of steel used in the construction sector in Senegal. Type 1 (E1), Type 2 (E2) and Type 3 (E3) steels are produced by locally established companies and Type 4 (E4) witness bars are imported from the France. The chemical analyses of the different types of steel were carried out by combustion, infrared (IR) detection for carbon and sulfur, by reducing fusion for nitrogen and by optical emission spectrometer (SEO) for the rest of the elements. The composition was determined on bars with a diameter of 10 mm. Linear mass deviations were evaluated for steels with a diameter of 8 mm, 10 mm and 12 mm. The results of the chemical analyses showed that the limit value for the percentage of carbon was exceeded by 29.16% for the steel, type 3. For the other types (1, 2 and 4), the limit values set out in the French standard NF EN 10,080 are not exceeded. As regards the relative differences in mass, the results showed that for steels of local manufacture, all the samples of bars with diameters 10 and 12 mm and 33% of steels with diameters 8 mm do not comply with the standard. The results also indicate that the chemical composition and relative linear mass deviations of the steels, type 4 comply with the standard. Thus, locally manufactured steels are not always suitable for use in reinforced concrete constructions.

基于端到端深度强化学习求解有能力约束的车辆路径问题

有能力约束的车辆路径问题(CVRP)是现阶段供应链应用最常见的问题模型,现多采用启发式算法求解。但随着问题规模增大,启发式算法求解速度慢且无法保证解的质量。提出端到端深度强化学习(DRL)网络框架对CVRP进行研究。首先利用边聚合图注意力网络编码器(EGATE)对车辆路径规划问题的图表示进行特征嵌入编码;然后设计多头注意力解码器(MAD)进行解码,并提出多解码策略以增加解的空间多样性;接着利用带回滚基线的基线REINFORCE算法对端到端网络模型进行训练,基线可自适应性更新以提升模型训练效果,并利用奖励函数归一化和Adam优化器对算法进行优化。最后通过对不同规模问题的实验以及与其他算法进行对比,验证了所提出端到端DRL框架的可行性与有效性,经过训练的模型在CVRPLIB公共数据集上的平均求解时间仅需0.189 s即可得到较优解。

Machine learning applications in stroke medicine:advancements,challenges,and future prospectives

Stroke is a leading cause of disability and mortality worldwide,necessitating the development of advanced technologies to improve its diagnosis,treatment,and patient outcomes.In recent years,machine learning techniques have emerged as promising tools in stroke medicine,enabling efficient analysis of large-scale datasets and facilitating personalized and precision medicine approaches.This abstract provides a comprehensive overview of machine learning’s applications,challenges,and future directions in stroke medicine.Recently introduced machine learning algorithms have been extensively employed in all the fields of stroke medicine.Machine learning models have demonstrated remarkable accuracy in imaging analysis,diagnosing stroke subtypes,risk stratifications,guiding medical treatment,and predicting patient prognosis.Despite the tremendous potential of machine learning in stroke medicine,several challenges must be addressed.These include the need for standardized and interoperable data collection,robust model validation and generalization,and the ethical considerations surrounding privacy and bias.In addition,integrating machine learning models into clinical workflows and establishing regulatory frameworks are critical for ensuring their widespread adoption and impact in routine stroke care.Machine learning promises to revolutionize stroke medicine by enabling precise diagnosis,tailored treatment selection,and improved prognostication.Continued research and collaboration among clinicians,researchers,and technologists are essential for overcoming challenges and realizing the full potential of machine learning in stroke care,ultimately leading to enhanced patient outcomes and quality of life.This review aims to summarize all the current implications of machine learning in stroke diagnosis,treatment,and prognostic evaluation.At the same time,another purpose of this paper is to explore all the future perspectives these techniques can provide in combating this disabling disease.

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation

Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment.

Experimental study on the durability of BFRP bars embedded in concrete

This paper presents an experimental study on the alkali-resistant properties of basalt fiber reinforced polymers （BFRP） bars under a typical concrete environment. BFRP bars were embedded in concrete and exposed to different aggressive environments, including tap water, saline solution and ambient temperature environments, to study the effects of the type of solution and relative humidity （RH） on the durability of BFRP. Meanwhile, BFRP bars were directly immersed in an alkaline solution for comparison. The acceleration factor describing the relationship between the alkaline solution immersion and the moisture-saturated concrete was also obtained. Aging was accelerated with a temperature of 60 ℃. The results show that the chloridion in the saline solution does not have any harmful effects on the degradation of the concrete-encased BFRP bars. Contact with an alkaline （high pH） concrete pore-water solution is the primary reason for the degradation of the BFRP bars. The degradation rate of concrete-encased BFRP bars is accelerated when a high temperature and a high humidity are present simultaneously. The degradation rate of the BFRP bars is relatively quick at the initial stage and slows down with exposure time. Results show that the degradation of 2.18 years in moisture-saturated concrete at 60 ℃corresponds to that of one year when directly immersed in an alkaline solution （other conditions remaining the same） for the BFRP bars analyzed.

UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.

Shear behavior of single-joint bolted sandstone subjected to dryewet cycles:Experimental and analytical approaches

A series of direct shear tests under constant normal loading conditions were carried out on specimens of bolted sandstone single-joint treated with different numbers of dryewet cycles.The experimental results show that the peak shear strength and shear stiffness of bolted sandstone joints were significantly reduced after 12 dryewet cycles.The decrease in the shear strength of rough joints is more significant than that of flat joints.Due to the decrease in the strength of the surrounding rock,the deformation characteristics of the bolts are significantly affected by the number of dryewet cycles performed.With an increase in the number of dryewet cycles,the plastic hinge length of the bolt gradually increases,resulting in an increase in the corresponding shear displacement when the bolt breaks.Compared with the tensileeshear failure mode of the bolts in flat joints,the tensileebending failure mode arises for bolts in rough joints.A shear curve model describing the whole process of bolted rock joints is established based on the deterioration of rock mechanical parameters caused by dry‒wet cycles.The model proposed considers the change in the friction angle of the joint surface with the shear displacement,which is applied to the derivation of the model by introducing the dynamic evolutionary friction angle parameter.The reasonably good agreement between a predicted curve and the corresponding experimental curve indicates that this method can effectively predict the shear strength of a bolted rock joint involving rough joint under dryewet cycling conditions.

Cognitive interference decision method for air defense missile fuze based on reinforcement learning

To solve the problem of the low interference success rate of air defense missile radio fuzes due to the unified interference form of the traditional fuze interference system,an interference decision method based Q-learning algorithm is proposed.First,dividing the distance between the missile and the target into multiple states to increase the quantity of state spaces.Second,a multidimensional motion space is utilized,and the search range of which changes with the distance of the projectile,to select parameters and minimize the amount of ineffective interference parameters.The interference effect is determined by detecting whether the fuze signal disappears.Finally,a weighted reward function is used to determine the reward value based on the range state,output power,and parameter quantity information of the interference form.The effectiveness of the proposed method in selecting the range of motion space parameters and designing the discrimination degree of the reward function has been verified through offline experiments involving full-range missile rendezvous.The optimal interference form for each distance state has been obtained.Compared with the single-interference decision method,the proposed decision method can effectively improve the success rate of interference.