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When the reinforced concrete beams are reinforced by bonding steel plates to the bottom, excessive use of steel plates will make the reinforced concrete beams become super-reinforced beams, and there are security risks in the actual use of super-reinforced beams. In order to avoid the occurrence of this situation, the purpose of this paper is to study the calculation method of the maximum number of bonded steel plates to reinforce reinforced concrete beams.

First of all, when establishing the limit failure state of the reinforced member, this paper comprehensively considers the role of the tensile steel bar and steel plate and takes the load effect before reinforcement as the negative contribution of the maximum number of bonded steel plates that can be used for reinforcement. Through the definition of the equivalent tensile strength, equivalent elastic modulus and equivalent yield strain of the tensile steel bar and steel plate, a method to determine the relative limit compression zone height of the reinforced member is obtained. Second, based on the maximum ratio of (reinforcement + steel plate), the relative limit compression zone height and the equivalent tensile strength of the tensile steel bar and steel plate of the reinforced member, the calculation method of the maximum number of bonded steel plates is derived. Then, the static load test of the test beam is carried out and the corresponding numerical model is established, and the reliability of the numerical model is verified by comparison. Finally, the accuracy of the calculation method of the maximum number of bonded steel plates is proved by the numerical model.

The numerical simulation results show that when the steel plate width is 800 mm and the thickness is 1–4 mm, the reinforced concrete beam has a delayed yield platform when it reaches the limit state, and the failure mode conforms to the basic stress characteristics of the balanced-reinforced beam. When the steel plate thickness is 5–8 mm, the sudden failure occurs without obvious warning when the reinforced concrete beam reaches the limit state. The failure mode conforms to the basic mechanical characteristics of the super-reinforced beam failure, and the bending moment of the beam failure depends only on the compressive strength of the concrete. The results of the calculation and analysis show that the maximum number of bonded steel plates for reinforced concrete beams in this experiment is 3,487 mm². When the width of the steel plate is 800 mm, the maximum thickness of the steel plate can be 4.36 mm. That is, when the thickness of the steel plate, the reinforced concrete beam is still the balanced-reinforced beam. When the thickness of the steel plate, the reinforced concrete beam will become a super-reinforced beam after reinforcement. The calculation results are in good agreement with the numerical simulation results, which proves the accuracy of the calculation method.

This paper presents a method for calculating the maximum number of steel plates attached to the bottom of reinforced concrete beams. First, based on the experimental research, the failure mode of reinforced concrete beams with different number of steel plates is simulated by the numerical model, and then the result of the calculation method is compared with the result of the numerical simulation to ensure the accuracy of the calculation method of the maximum number of bonded steel plates. And the study does not require a large number of experimental samples, which has a certain economy. The research result can be used to control the number of steel plates in similar reinforcement designs.

This paper aim to solve the problem of low assembly success rate for 3c assembly lines designed based on classical control algorithms due to inevitable random disturbances and other factors,by incorporating intelligent algorithms into the assembly line, the assembly process can be extended to uncertain assembly scenarios.

This work proposes a reinforcement learning framework based on digital twins. First, the authors used Unity3D to build a simulation environment that matches the real scene and achieved data synchronization between the real environment and the simulation environment through the robot operating system. Then, the authors trained the reinforcement learning model in the simulation environment. Finally, by creating a digital twin environment, the authors transferred the skill learned from the simulation to the real environment and achieved stable algorithm deployment in real-world scenarios.

In this work, the authors have completed the transfer of skill-learning algorithms from virtual to real environments by establishing a digital twin environment. On the one hand, the experiment proves the progressiveness of the algorithm and the feasibility of the application of digital twins in reinforcement learning transfer. On the other hand, the experimental results also provide reference for the application of digital twins in 3C assembly scenarios.

In this work, the authors designed a new encoder structure in the simulation environment to encode image information, which improved the model’s perception of the environment. At the same time, the authors used the fixed strategy combined with the reinforcement learning strategy to learn skills, which improved the rate of convergence and stability of skills learning. Finally, the authors transferred the learned skills to the physical platform through digital twin technology and realized the safe operation of the flexible printed circuit assembly task.

In the analysis of structures in a fire situation by simplified and analytical methods, one assumption is that the fire resistance time is greater than or equal to the required fire resistance time. Among the methodologies involving the fire resistance time, the most used is the tabular method, which associates fire resistance time values to structural elements based on minimum dimensions of the cross section. The tabular method is widely accepted by the technical-scientific community due to the fact that it is safe and practical. However, its main criticism is that it results in lower fire resistance times than advanced thermal and thermostructural analysis methods. The objective of this study was to evaluate the fire resistance time of reinforced concrete beams and compare it with the required fire resistance time recommended by the tabular method of NBR 15200 (ABNT, 2012).

The fire resistance time and required fire resistance time of reinforced concrete beams were evaluated using, respectively, numerical models developed based on the finite element method and the tabular method of NBR 15200 (ABNT, 2012). The influence of the following parameters was investigated: longitudinal reinforcement cover, characteristic compressive strength of concrete, beam height, longitudinal reinforcement area and arrangement of steel bars.

Among the evaluated parameters, the covering of the longitudinal reinforcement proved to be more relevant for the fire resistance time, justifying that the tabular method of NBR 15200 (ABNT, 2012) being strongly and directly influenced by this parameter. In turn, more resistant concretes, higher beams and higher steel grades have lower fire resistance time values. This is because beams in these conditions have greater resistance capacity at room temperature and, consequently, are subject to external stresses of greater magnitude. In some cases, the fire resistance time was even lower than the required fire resistance time prescribed by NBR 15200 (ABNT, 2012). Both the fire resistance time and the required fire resistance time were not influenced by the arrangement of the longitudinal reinforcements.

The present paper innovates by demonstrating the influence of other important design variables on the required fire resistance time of the NBR 15200 (ABNT, 2012). Among several conclusions, it was found that the load level to which the structural elements are subjected considerably affects their fire resistance time. For this reason, it was recommended that the methods for calculating the required fire resistance time consider the load level. In addition, the article quantifies the security degree of the tabular method and exposes some situations for which the tabular method proved to be unsafe. Moreover, in all the models analyzed, the relationship between the span and the vertical deflection associated with the failure of the beams in a fire situation was determined. With this, a span over average deflection relationship was presented in which beams in fire situations fail.

This research outlines the development and characterization of advanced composite materials and their potential applications in the aerospace industry for interior applications. Advanced composites, such as carbon-fiber-reinforced polymers and ceramic matrix composites, offer significant advantages over traditional metallic materials in terms of weight reduction, stiffness and strength. These materials have been used in various aerospace applications, including aircraft, engines and thermal protection systems.

The development of design of experiment–based hybrid aluminum composites using the stir-casting technique has further enhanced the performance and cost-effectiveness of these materials. The design of the experiment was followed to fabricate hybrid composites with nano cerium oxide (nCeO₂) and graphene nanoplatelets (GNPs) as reinforcements in the Al-6061 matrix.

The Al6061 + 3% nCeO2 + 3% GNPs exhibited a high hardness of 119.6 VHN. The ultimate tensile strength and yield strength are 113.666 MPa and 73.08 MPa, respectively. A uniform distribution of reinforcement particulates was achieved with 3 Wt.% of each reinforcement in the matrix material, which is analyzed using scanning electron microscopy. Fractography revealed that brittle and ductile fractures caused the failure of the fractured specimens in the tensile test.

The manufactured aluminum composite can be applied in a range of exterior and interior structural parts like wings, wing boxes, motors, gears, engines, antennas, floor beams, etc. The fan case material of the GEnx engine (currently using carbon-fiber reinforcement plastic) for the Boeing 7E7 can be another replacement with manufactured hybrid aluminum composite, which predicts weight savings per engine of close to 120 kg.

The development of hybrid reinforcements, where two or more types of reinforcements are used in combination, is also a novel approach to improving the properties of these composites. Advanced composite materials are known for their high strength-to-weight ratio. If the newly developed composite material demonstrates superior properties, it can potentially be used to replace traditional materials in aircraft manufacturing. By reducing the weight of aircraft structures, fuel efficiency can be improved, leading to reduced operating costs and environmental impact. This allows for a more customized solution for specific application requirements and can lead to further advancements in materials science and technology.

The purpose of this paper is to study Al-based green composite. To make composite samples of aluminium alloy (AA3105) with different weight percentages of rice husk ash (RHA) and eggshell (ES) particles as reinforcement, stir casting method was used.

Several other aspects, including the weight percent of reinforcing agent particles, the applied stress and the sliding speed, were taken into consideration. During the course of the wear test, the sliding distance that was recorded varied from a minimum of 1,000 m all the way up to a maximum of 3,135 m (10, 15, 20, 25 and 30 min). The typical range for normal loads is 8–24 N, and their speed is 1.58 m/s.

With the AA/ES/RHA composite, the wear rates decreases when the grain size of the reinforcing particles enhanced. Scanning electron microscopy images of worn surfaces show that at low speeds, delaminating and ploughing are the main causes of wear. At high speeds, ploughing is major cause of wear. Composites with better wear-resistant properties can be used in wide range of tribological applications, especially in the automotive industry. It was found that hardness increases at the same time as the weight of the reinforcement increases. Tensile and hardness were maximized at 10% reinforcement mix in Al3105.

In this work, ES and RHA has been used to develop green metal matrix composite to support green revolution as promoted/suggested by United Nations thus reducing the environmental pollution.

At present, teaching methods based on 2D drawings are still commonly used for educating students on the location of steel reinforcement bars in concrete. However, traditional teaching methods have limitations as students can find it difficult to understand 2D drawings. This study aims to develop an interactive and collaborative augmented reality environment (ICARE) using augmented reality (AR) technology to improve students' engagement in learning.

This study develops an ICARE prototype, which is organized into two stages: (1) The augmented teaching environment comprising of models and interactive components; (2) The AR collaborative application which uses Photon Unity Networking (PUN) plugin and Azure spatial anchors cloud service. The AR-based teaching environment runs with Universal Windows Platform (UWP) to enable development in the HoloLens 2 through Microsoft Visual Studio.

An experimental study was conducted, where 60 students were divided into three groups employing Drawings-based, building information modeling (BIM)-based and AR-based methods for teaching. After the test, the three groups of students were requested to complete a questionnaire. According to the analysis of the experimental results, the ICARE can improve students' comprehension, memory of learned materials and their ability to read and understand steel reinforcement drawings improving the quality of teaching, especially interactivity and engagement.

As illustrated in the experiments, the developed ICARE has outstanding performance over conventional approaches in civil engineering courses that can improve students' comprehension and memory of knowledge and their ability to read and understand steel bar drawings. This study provides empirical evidence that AR is a promising technology that can be integrated with traditional classroom instruction and can improve students' comprehension and memory of knowledge and their ability to read and understand steel bar drawings.

The purpose of this study to find an alternate method to minimize waste i.e., eggshell and rice husk ash. In this paper, eggshell (ES) and rice husk ash (RHA) particles are used as reinforcements for examining their effect on the coefficient of thermal expansion (CTE), grain size (GS) and corrosion behavior for developed composite material.

In this investigation, 5 Wt.% each of ES and RHA reinforcement particles have been introduced. To investigate the microstructures of the developed composite material, scanning electron microscope was used. Physical and mechanical properties of composite material are tensile strength and hardness that have been examined.

The result of this paper shows that number of grains per square inch for composition Al/5% ES/5% RHA composite was found to be 1,243. Minimum value of the volume CTE was found to be 6.67 × 10–6/°C for Al/5% ES/5% RHA composite. The distribution of hard phases of ES particles in metal matrix is responsible for improvements in tensile strength and hardness. These findings demonstrated that using carbonized ES as reinforcement provides superior mechanical and physical properties than using uncarbonized ES particles.

There are several articles examining the impact of varying Wt.% of carbonized ES and rice husk reinforcement on the microstructures and mechanical characteristics of metal composites. CTE, GS and corrosion behavior are among of the features that are examined in this paper.

This paper explores optimal service resource management strategy, a continuous challenge for health information service to enhance service performance, optimise service resource utilisation and deliver interactive health information service.

An adaptive optimal service resource management strategy was developed considering a value co-creation model in health information service with a focus on collaborative and interactive with users. The deep reinforcement learning algorithm was embedded in the Internet of Things (IoT)-based health information service system (I-HISS) to allocate service resources by controlling service provision and service adaptation based on user engagement behaviour. The simulation experiments were conducted to evaluate the significance of the proposed algorithm under different user reactions to the health information service.

The results indicate that the proposed service resource management strategy, considering user co-creation in the service delivery, process improved both the service provider’s business revenue and users' individual benefits.

The findings may facilitate the design and implementation of health information services that can achieve a high user service experience with low service operation costs.

This study is amongst the first to propose a service resource management model in I-HISS, considering the value co-creation of the user in the service-dominant logic. The novel artificial intelligence algorithm is developed using the deep reinforcement learning method to learn the adaptive service resource management strategy. The results emphasise user engagement in the health information service process.

This research paper aims to investigate reinforced concrete (RC) walls' behaviour under fire and identify the thermal and mechanical factors that affect their performance.

A three-dimensional (3D) finite element (FE) model is developed to predict the response of RC walls under fire and is validated through experimental tests on RC wall specimens subjected to fire conditions. The numerical model incorporates temperature-dependent properties of the constituent materials. Moreover, the validated model was used in a parametric study to inspect the effect of the fire scenario, reinforcement concrete cover, reinforcement ratio and configuration, and wall thickness on the thermal and structural behaviour of the walls subjected to fire.

The developed 3D FE model successfully predicted the response of experimentally tested RC walls under fire conditions. Results showed that the fire resistance of the walls was highly compromised under hydrocarbon fire. In addition, the minimum wall thickness specified by EC2 may not be sufficient to achieve the desired fire resistance under considered fire scenarios.

There is limited research on the performance of RC walls exposed to fire scenarios. The study contributed to the current state-of-the-art research on the behaviour of RC walls of different concrete types exposed to fire loading, and it also identified the factors affecting the fire resistance of RC walls. This guides the consideration and optimisation of design parameters to improve RC walls performance in the event of a fire.

To contribute to the identification of the parameters influencing the behavior of textile-reinforced concrete (TRC), the purpose of this paper is to investigate the flexural behavior of TRC-based plates under four-point bending notably designed in the context of sustainable development and the substitution of mortar components with natural and abundant materials.

An extensive experimental campaign was focused about two main parameters. The first one emphases the textile reinforcements, such as the number of layers, the nature and the textile mesh size. In the second step, the composition of the mortar matrix was explored through the use of dune sand as a substitute of the river one.

Test results in terms of load-displacement response and failure patterns were highlighted, discussed and confronted to literature ones. As key findings, an increase of the load-bearing capacity and ductility, comparable to the use of an industrially produced second textile layer was recorded with the use of dune sand in the mortar mix design. The designed ecofriendly samples with economic concerns denote the significance of obtained outcomes in this research study.

The novelty of the present work was to valorize the use of natural dune sand to design new TRC samples to respond to the environmental and economical requirements. The obtained values provide an improved textiles–matrix interface performance compared to classical TRC samples issued from the literature.