Traditional Reinforcement Research Articles

Energy Management Strategy Based on Reinforcement Learning and Frequency Decoupling for Fuel Cell Hybrid Powertrain

This study presents a Two-Layer Deep Deterministic Policy Gradient (TL-DDPG) energy management strategy for Hydrogen fuel cell hybrid train, that aims to solve the problem that traditional reinforcement learning strategies require high initial values and are difficult to optimize global variables. Augmenting the optimization capabilities of the inner layer, a frequency decoupling algorithm integrates into the outer layer, furnishing a fitting initial value for strategy optimization. This addition aims to bolster the stability of fuel cell output, thereby enhancing the overall efficiency of the hybrid power system. In comparison with the traditional reinforcement learning algorithm, the proposed approach demonstrates notable improvements: a reduction in hydrogen consumption per 100 km by 16.3 kg, a 9.7% increase in the output power stability of the fuel cell, and a 1.8% enhancement in its efficiency.

Safe adaptive output‐feedback optimal control of a class of linear systems

AbstractThe objective of this research is to enable safety‐critical systems to simultaneously learn and execute optimal control policies in a safe manner to achieve complex autonomy. Learning optimal policies via trial and error, that is, traditional reinforcement learning, is difficult to implement in safety‐critical systems, particularly when task restarts are unavailable. Safe model‐based reinforcement learning techniques based on a barrier transformation have recently been developed to address this problem. However, these methods rely on full‐state feedback, limiting their usability in a real‐world environment. In this work, an output‐feedback safe model‐based reinforcement learning technique based on a novel barrier‐aware dynamic state estimator has been designed to address this issue. The developed approach facilitates simultaneous learning and execution of safe control policies for safety‐critical linear systems. Simulation results indicate that barrier transformation is an effective approach to achieve online reinforcement learning in safety‐critical systems using output feedback.

Experimental and numerical analysis of tunnel primary support using recycled, and hybrid fiber reinforced shotcrete

Shotcrete plays a pivotal role in the construction of tunnels and underground structures; however, its inherent brittleness necessitates reinforcement to enhance ductility. This research explores the use of fiber-reinforced shotcrete as primary tunnel support to enhance ductility and reduce brittleness. Traditional steel mesh reinforcement complexities have led to the investigation of alternative materials. The research evaluates different fiber mix designs, including industrial steel, recycled steel fibers from tires, and Forta fibers, examining their strength parameters and deformation performance. A 3D finite element model is used to simulate a horseshoe-shaped tunnel with optimal mix design and plain shotcrete in a soil environment. The study finds that hybrid industrial and recycled fibers are more effective than single fibers, enhancing compressive, tensile, and flexural strength and reducing ground surface settlement and tensile damage. The optimal mix design of this study has increased compressive, tensile, and flexural strength, as well as flexural toughness, compared to plain shotcrete. Numerical modeling reveals that utilizing fiber reinforced shotcrete made out of optimal fiber mix design as primary support results in a significant reduction in ground surface settlement and tensile damage value. Furthermore, the study shows a significant reduction in the damaged zone area under tensile stresses. The results of the study highlight the potential of fiber reinforced shotcrete as a primary support for tunnels, leading to improved performance and sustainability in tunnel construction.

Development of Eco-Friendly Soil Improvement Agent for Enhanced Slope Stability and Erosion Control in Urban Areas

Due to the impact of climate change, extreme rainfall events are becoming more frequent, resulting in shallow slope collapse and erosion that trigger debris flows. While traditional reinforcement methods like anchoring and nailing are effective, they can be costly and environmentally unfriendly. To address this issue, researchers have investigated using in situ soil reinforcement with vegetation, which is a more sustainable and economical option. In this study, a soil improvement agent was developed using leaf mold and herbal medicine to promote vegetation growth. Adding microcement and gypsum hemihydrate increased the shear strength of the soil, preventing surface erosion. A laboratory test confirmed that the combination of these ingredients effectively increased the soil’s resistance to erosion caused by rainfall. The soil improvement agent proposed in this study was applied to the case of the slope failure in the Gwangju area, South Korea, to confirm the slope stability for 10 days of rainfall. The results of numerical analysis confirmed that the reinforced slope cured by the pozzolanic reaction using the developed material improved the slope stability by 36% compared to the original soil slope during the rainy season.

Advancing spacecraft rendezvous and docking through safety reinforcement learning and ubiquitous learning principles

As spacecraft rendezvous and docking missions become increasingly complex, the need for advanced solutions has surged. In recent years, the application of reinforcement learning techniques to tackle spacecraft rendezvous guidance challenges has emerged as a prominent international trend. Vital to ensuring the secure rendezvous and docking of spacecraft is the task of obstacle avoidance. However, traditional reinforcement learning algorithms lack the ability to enforce safety constraints within the exploration space, which presents a formidable obstacle in the design of spacecraft rendezvous guidance strategies. In response to this challenge, a spacecraft rendezvous guidance methodology founded on safety reinforcement learning is proposed. Firstly, a Markov model is crafted for autonomous spacecraft rendezvous in scenarios involving collision avoidance. A reward system, contingent on obstacle warnings and collision avoidance constraints, is introduced to establish a safety reinforcement learning framework for devising spacecraft rendezvous guidance strategies. Secondly, within the framework of safety reinforcement learning, two deep reinforcement learning (DRL) algorithms, Proximal Policy Optimisation (PPO) and Deep Deterministic Policy Gradient (DDPG), are leveraged to generate these guidance strategies. Experimental findings validate the effectiveness of this approach in successfully executing obstacle avoidance and achieving rendezvous with remarkable precision. Furthermore, through an analysis of the performance and generalization capabilities of these two algorithms, the efficacy of the proposed methodology is further underscored. This fusion of advanced space guidance technology with the principles of Ubiquitous Learning marks a significant step forward in the quest for safer and more efficient spacecraft rendezvous and docking operations.

How to Design Reinforcement Learning Methods for the Edge: An Integrated Approach toward Intelligent Decision Making

Extensive research has been carried out on reinforcement learning methods. The core idea of reinforcement learning is to learn methods by means of trial and error, and it has been successfully applied to robotics, autonomous driving, gaming, healthcare, resource management, and other fields. However, when building reinforcement learning solutions at the edge, not only are there the challenges of data-hungry and insufficient computational resources but also there is the difficulty of a single reinforcement learning method to meet the requirements of the model in terms of efficiency, generalization, robustness, and so on. These solutions rely on expert knowledge for the design of edge-side integrated reinforcement learning methods, and they lack high-level system architecture design to support their wider generalization and application. Therefore, in this paper, instead of surveying reinforcement learning systems, we survey the most commonly used options for each part of the architecture from the point of view of integrated application. We present the characteristics of traditional reinforcement learning in several aspects and design a corresponding integration framework based on them. In this process, we show a complete primer on the design of reinforcement learning architectures while also demonstrating the flexibility of the various parts of the architecture to be adapted to the characteristics of different edge tasks. Overall, reinforcement learning has become an important tool in intelligent decision making, but it still faces many challenges in the practical application in edge computing. The aim of this paper is to provide researchers and practitioners with a new, integrated perspective to better understand and apply reinforcement learning in edge decision-making tasks.

Learning strategies for underwater robot autonomous manipulation control

Autonomous manipulation operations represent the high intelligent coordination from robotic vision and control, it is also a symbol of the advances of robotic intelligence. The limitations of visual sensing and the increasingly complex experimental conditions make autonomous manipulation operations more difficult, particularly for deep reinforcement learning methods, which can enhance robotic control intelligence but require a lot of training process. Due to the high-dimensional continuous state space and continuous action space characteristics of underwater operations, this paper adopts a policy-based reinforcement learning method as the foundational approach. To address the issues of instability and low convergence efficiency in traditional policy-based reinforcement learning algorithms during the learning process, this paper proposes a novel policy learning method. This method adopts the Proximal Policy Optimization algorithm (PPOClip) and optimizes it through an actor-critic network. The aim is to improve the stability and effectiveness of convergence in the learning process. In the underwater training environment, a new reward shaping scheme has been designed to address the issue of reward sparsity during the training process. The manually crafted dense reward function is utilized as attractive and repulsive potential functions for goal manipulation and obstacle avoidance. On the highly complex underwater manipulation and training environment, transferred learning algorithm has been established to reduce the training times and compensate the differences between the simulation and experiment. Simulations and tank experiments have verified the performance of the proposed strategy learning method.

A Survey of Deep Reinforcement Learning Based on Multi-Particle Environments

This paper extensively explores the application of deep reinforcement learning within multi-agent environments, a domain marked by intricate challenges and burgeoning opportunities. Multi-agent environments inherently entail the presence of multiple interacting intelligent agents, thereby amplifying the complexity of decision-making and control. Traditional reinforcement learning methods, when confronted with these intricate settings, manifest limitations that have prompted a compelling shift towards the fusion of deep learning and reinforcement learning. The journey commences by elucidating the multifaceted challenges and intricacies pervasive in multi-agent environments. It systematically delineates the constraints of conventional reinforcement learning techniques, underscoring their inadequacies in effectively addressing the multifarious complexities presented by these environments. As the narrative unfolds, it places a spotlight on the transformative potential of deep learning in surmounting the formidable challenges associated with decision-making in multi-agent settings. A critical examination ensues, encompassing a thorough review of pioneering applications of methodologies such as value functions, policy gradients, and actor-critic approaches within the realm of multi-agent systems. Through an exhaustive exploration of existing literature, this article endeavors to unveil the vanguard developments and persisting challenges within the domain of deep reinforcement learning in multi-agent environments. Furthermore, this paper conducts a comparative analysis of the performance of two prominent algorithms, namely Q-learning and the Deep Q Network (DQN) algorithm. This analysis serves as a compass, elucidating the trajectory of development within the sphere of deep reinforcement learning. Ultimately, this paper acts as a compass, guiding future research and innovation in the dynamic and promising field of deep reinforcement learning within multi-agent environments.

Finite element method for sustainable and resilient structures made with bar and fiber -reinforced EAFS concrete

Structural engineers have to address the climate change challenge by designing sustainable and resilient structures. At this juncture, Electric Arc Furnace Slags (EAFS), a steel-industry waste, are used in replacement of natural aggregates to enhance concrete properties. Moreover, steel and synthetic fibers are added to improve the postcracking behavior while the traditional bar reinforcement enhances the tensile performance. This makes EAFS concrete substantially ductile compared to normal concrete, which contributes to a higher structural resiliency, and hence minimizes functionality disruptions. However the use of fiber and bar -reinforced EAFS concrete in structures is still limited due to the uncertainties introduced by EAFS and fibers. This justify the development of advanced modeling techniques (ie. Finite element Analysis, FEA), which can be used to predict the behavior of EAFS concrete structures at the designing stage. This work build up from the extensive work of the coauthors in the testing of EAFS concrete and, more recently, in the developed FEA of fiber-reinforced EAFS concrete. In this paper the modeling of bar reinforcement is added to the FEA to study the behavior of structural elements made with fiber-reinforced EAFS concrete. The presented FEA is validated through full-scale experiments (four-point flexural test), which shows that the presented FEA is appropriate. The presented numerical model enables to study phenomena difficult to study from experiments or in-situ such as the cracking. It is worth noting that the addition of steel fibers reduced the crack mouth opening displacement in 29.3% and the depth of the cracks in 12.7% in the presented EAFS concrete.

Duty Cycle Scheduling in Wireless Sensor Networks Using an Exploratory Strategy-Directed MADDPG Algorithm

This paper presents an in-depth study of the application of Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithms with an exploratory strategy for duty cycle scheduling (DCS) in the wireless sensor networks (WSNs). The focus is on optimizing the performance of sensor nodes in terms of energy efficiency and event detection rates under varying environmental conditions. Through a series of simulations, we investigate the impact of key parameters such as the sensor specificity constant α and the Poisson rate of events on the learning and operational efficacy of sensor nodes. Our results demonstrate that the MADDPG algorithm with an exploratory strategy outperforms traditional reinforcement learning algorithms, particularly in environments characterized by high event rates and the need for precise energy management. The exploratory strategy enables a more effective balance between exploration and exploitation, leading to improved policy learning and adaptation in dynamic and uncertain environments. Furthermore, we explore the sensitivity of different algorithms to the tuning of the sensor specificity constant α, revealing that lower values generally yield better performance by reducing energy consumption without significantly compromising event detection. The study also examines the algorithms' robustness against the variability introduced by different event Poisson rates, emphasizing the importance of algorithm selection and parameter tuning in practical WSN applications. The insights gained from this research provide valuable guidelines for the deployment of sensor networks in real-world scenarios, where the trade-off between energy consumption and event detection is critical. Our findings suggest that the integration of exploratory strategies in MADDPG algorithms can significantly enhance the performance and reliability of sensor nodes in WSNs.

Stable training via elastic adaptive deep reinforcement learning for autonomous navigation of intelligent vehicles

The uncertain stability of deep reinforcement learning training on complex tasks impedes its development and deployment, especially in intelligent vehicles, such as intelligent surface vessels and self-driving cars. Complex and varied environmental states puzzle training of decision-making networks. Here we propose an elastic adaptive deep reinforcement learning algorithm to address these challenges and achieve autonomous navigation in intelligent vehicles. Our method trains the decision-making network over the function and optimization learning stages, in which the state space and action space of autonomous navigation tasks are pruned by choosing classic states and actions to reduce data similarity, facilitating more stable training. We introduce a task-adaptive observed behaviour classification technique in the function learning stage to divide state and action spaces into subspaces and identify classic states and actions. In which the classic states and actions are accumulated as the training dataset that enhances its training efficiency. In the subsequent optimization learning stage, the decision-making network is refined through meticulous exploration and accumulation of datasets. The proposed elastic adaptive deep reinforcement learning enables the decision-making network to effectively learn from complex state and action spaces, leading to more efficient training compared to traditional deep reinforcement learning approaches. Simulation results demonstrate the remarkable effectiveness of our method in training decision-making networks for intelligent vehicles. The findings validate that our method provides reliable and efficient training for decision-making networks in intelligent vehicles. Moreover, our method exhibits stability in training other tasks characterized by continuous state and action spaces.

Energy Management for Hybrid Electric Vehicles Using Safe Hybrid-Action Reinforcement Learning

Reinforcement learning has shown success in solving complex control problems, yet safety remains paramount in engineering applications like energy management systems (EMS), particularly in hybrid electric vehicles (HEVs). An effective EMS is crucial for coordinating power flow while ensuring safety, such as maintaining the battery state of charge within safe limits, which presents a challenging task. Traditional reinforcement learning struggles with safety constraints, and the penalty method often leads to suboptimal performance. This study introduces Lagrangian-based parameterized soft actor–critic (PASACLag), a novel safe hybrid-action reinforcement learning algorithm for HEV energy management. PASACLag utilizes a unique composite action representation to handle continuous actions (e.g., engine torque) and discrete actions (e.g., gear shift and clutch engagement) concurrently. It integrates a Lagrangian method to separately address control objectives and constraints, simplifying the reward function and enhancing safety. We evaluate PASACLag’s performance using the World Harmonized Vehicle Cycle (901 s), with a generalization analysis of four different cycles. The results indicate that PASACLag achieves a less than 10% increase in fuel consumption compared to dynamic programming. Moreover, PASACLag surpasses PASAC, an unsafe counterpart using penalty methods, in fuel economy and constraint satisfaction metrics during generalization. These findings highlight PASACLag’s effectiveness in acquiring complex EMS for control within a hybrid action space while prioritizing safety.

Deep Reinforcement Learning Unleashing the Power of AI in Decision-Making

Deep Reinforcement Learning (DRL) has emerged as a transformative paradigm in the field of artificial intelligence (AI), offering unprecedented capabilities in decision-making across diverse domains. This article explores the profound impact of DRL on enhancing the decision-making capabilities of AI systems, elucidating its underlying principles, applications, and implications.DRL represents a fusion of deep learning and reinforcement learning, enabling machines to learn complex behaviors and make decisions by interacting with their environment. The utilization of neural networks allows DRL algorithms to handle high-dimensional input spaces, making it well-suited for tasks that involve intricate decision-making processes.One of the key strengths of DRL lies in its ability to address problems with sparse and delayed rewards, common challenges in traditional reinforcement learning. Through a process of trial and error, DRL algorithms can learn optimal decision strategies by navigating through a vast decision space, adapting to dynamic environments, and maximizing cumulative rewards over time.The applications of DRL span various domains, including robotics, finance, healthcare, gaming, and autonomous systems. In robotics, DRL facilitates the development of intelligent agents capable of autonomously navigating complex environments, performing intricate tasks, and adapting to unforeseen circumstances. In finance, DRL is leveraged for portfolio optimization, algorithmic trading, and risk management, demonstrating its potential to revolutionize traditional financial strategies.

Effects of GFRP Stirrup Spacing on the Behavior of Doubly GFRP-Reinforced Concrete Beams

This study investigates the impact of varying glass fiber-reinforced polymer (GFRP) stirrup spacing on the performance of doubly GFRP-reinforced concrete beams. The research focuses on assessing the behavior of GFRP-reinforced concrete beams, including load-carrying capacity, cracking, and deformability. It explores the feasibility and effectiveness of GFRP bars as an alternative to traditional steel reinforcement in concrete structures. Six concrete beams with a cross-section of 300 mm (wide) × 250 mm (deep), simply supported on a 2100 mm span, were tested. The beams underwent four-point bending with two concentrated loads applied symmetrically at one-third of the span length, resulting in a shear span (a)-to-depth (h) ratio of 2.8. The experimental findings reveal that altering the GFRP stirrup spacing along the longitudinal axis of the beams, from 200 mm (equivalent to the effective depth (d)) to 50 mm (equal to (d⁄4)), altered the mode of failure from flexure-shear to flexure-compression. However, when the spacing was equal to or less than (d⁄3), there was no significant improvement in load-carrying capacity, as the contribution of GFRP bars in resisting shear loads was limited. Under service loads, the GFRP-reinforced beams exhibited wider cracks, but reducing the stirrup spacing helped restrain crack widening. Incorporating GFRP bars in the compression zone had a positive effect on reducing crack width in the tension zone. Additionally, using GFRP stirrups with spacing varying between (d) and (d⁄2) in the pure bending region increased the deflection ductility indexes. To enhance the ductility of GFRP-reinforced concrete beams, it is recommended to use GFRP stirrups in the pure bending region with spacing greater than the spacing between GFRP stirrups in the shear spans. The study highlights that the current ACI code overestimates the shear capacity provided by GFRP stirrups, particularly when the spacing is less than or equal to (d⁄3). Doi: 10.28991/CEJ-2024-010-02-011 Full Text: PDF

Electric vehicle charging navigation strategy in coupled smart grid and transportation network: A hierarchical reinforcement learning approach

Most of the existing electric vehicle (EV) charging navigation methods do not simultaneously take into account the electric vehicle charging destination optimization and path planning. Moreover, they are unable to provide online real-time decision-making under a variety of uncertain factors. To address these problems, this paper first establishes a bilevel stochastic optimization model for EV charging navigation considering various uncertainties, and then proposes an EV charging navigation method based on the hierarchical enhanced deep Q network (HEDQN) to solve the above stochastic optimization model in real-time. The proposed HEDQN contains two enhanced deep Q networks, which are utilized to optimize the charging destination and charging route path of EVs, respectively. Finally, the proposed method is simulated and validated in two urban transportation networks. The simulation results demonstrate that compared with the Dijkstra shortest path algorithm, single-layer deep reinforcement learning algorithm, and traditional hierarchical deep reinforcement learning algorithm, the proposed HEDQN algorithm can effectively reduce the total charging cost of electric vehicles and realize online real-time charging navigation of electric vehicles, that shows excellent generalization ability and scalability.

Research on Traditional Reinforcement Techniques for Rammed Earth Walls in China

ABSTRACT Rammed earth is typically a material with a high compressive capacity and low tensile and shear strength. In traditional Chinese architecture, reinforcing rammed-earth walls with ties and beams is common. This study offers insights into the characteristics of reinforcement methods applied to rammed earth walls in houses and forts. After analyzing the mechanical failure of the walls, the article suggests that reinforcement can enhance mechanical performance by reducing shrinkage cracks, enhancing the bearing capacity of walls, and increasing the integrity of the entire building.

Mitigating oscillations of mixed traffic flows at a signalized intersection: A multiagent trajectory optimization approach based on oscillation prediction

To mitigate the traffic oscillations generated when traffic flow encounters a red light at a signalized intersection, many studies have used reinforcement learning to control connected and automated vehicles (CAVs) trajectories to avoid oscillations. These methods, which are effective in mitigating oscillations in a pure CAV environment, perform poorly when applied to mixed traffic streams, due to the fact that most of the methods ignore that human vehicles (HVs) in mixed traffic flows stop at red lights and trigger oscillations, resulting in significant “trajectory fluctuation” phenomena in some CAVs, which will lead to poor effectiveness in mitigating oscillations.To address this problem, We propose a method that combines oscillation prediction and multiagent trajectory optimization to mitigate the oscillations generated by HVs. First, we predict the oscillation caused by HVs stopping during a red light, and after that, we use the Newell model to predict the propagation range of the oscillation. According to whether the HV ahead produces oscillation or not, we classify CAVs into two types of agents and design the control scheme separately. The first type of CAVs are immediately adjacent to the oscillation, and we use a jam-absorption driving (JAD) strategy to treat them as absorbing vehicles to absorb the oscillation. While the second type of CAVs are vehicles other than the first type of CAVs, we use the deep reinforcement learning (DRL) method to control them to arrive at the intersection within the predicted green light time. We tested the method in a mixed traffic flow simulation environment. Experimental results show that this method can effectively suppress the phenomenon of “trajectory fluctuations” under different CAV penetration rates, achieving the goal of effectively mitigating oscillations thus significantly improving traffic efficiency and reducing vehicle fuel consumption. Meanwhile, the method also has the advantages of fast convergence during training and high stability during testing compared with traditional reinforcement learning methods.

Improved Bond Stress-Slip Relationships for Carbon Fibre-Reinforced Polymer-Strengthened Masonry Triplets

Carbon fibre-reinforced polymer (CFRP) emerges as a viable solution for reinforcing unreinforced masonry (URM) walls subjected to shear loads. While masonry structures are straightforward to construct, the complexity of the construction materials, especially in terms of their mechanical properties, poses challenges for numerical studies of their structural behaviour. Walls, being fundamental components in masonry construction, play a crucial role in transferring both horizontal and vertical lateral forces. This study investigates the enhancement of masonry wall behaviour through the reinforcement of CFRP. CFRP reinforcement increases ductility and strength, reducing the risk of failure under shear conditions. Additionally, CFRP composites present a practical solution to strengthening masonry structures compared to traditional reinforcement. However, brick, mortar, and CFRP have not been thoroughly investigated. Experimental tests on the bond behaviour of different configurations of CFRP-retrofitted masonry triplets have not been performed before and are therefore presented in this paper. Triplet specimens, comprising three bricks and two mortar joints, both with and without CFRP strengthening, were subjected to bond testing. The study affirms that masonry triplets strengthened with CFRP under shear loads exhibit strength levels at least four to six times greater than those without CFRP. The experimental work was carried out with eight different CFRP configurations on triplet masonry, and each test was repeated four times. Further, the bond stress-slip relationship in the case of masonry triplets with and without CFRP was predicted with new mathematical equations based on the conducted test results. These equations were included in the commercial finite element software ANSYS and used to conduct simulations of CFRP-reinforced masonry triplets. The numerical results indicate good agreement between the finite element model and the test results. The outcome of this research improves the current knowledge on the use of CFRP to reinforce masonry walls with brick and mortar, which will contribute to the understanding of the effect of CFRP on masonry structures.

Hybrid CFRP-BFRP Rebars for Prestress Concrete Slabs: Abaqus Analysis and Experimental Validation

This study utilizes Abaqus finite element software to analyze concrete slabs reinforced with a novel hybrid of Carbon Fiber Reinforced Polymers (CFRP) and Basalt Fiber Reinforced Polymers (BFRP) rebars known as CBRP rebars. The investigation focuses on assessing the structural performance of these slabs under varying prestressing levels (PL) and FRP reduction factors (FR), along with the prestress index level (PI). The study defines the FR factor as the reduction in the transverse reinforcement area of FRP compared to the control slab. The PI is the ratio of prestressed CBRP rebars to the total FRP reinforcement in the transverse direction, aiming to determine a lower FRP reduction factor without compromising the FRP slabs' design requirements. Three FR values (0.45, 0.37, and 0.29) are considered, with the CBRP tendon tensioned at 35% and 50% of the ultimate strength, including 0.5 and 0.6 of the PI, for validation against experimental test data. The numerical analysis incorporates advanced finite element modeling techniques, integrating material properties, load conditions, and structural parameters to detail the behavior of CBR-reinforced concrete slabs. Various loading scenarios are simulated to evaluate the performance of CBRP rebars under diverse conditions. Comparison with experimental data validates the hybrid reinforcement approach's reliability. The study's findings contribute significantly to understanding the structural performance of concrete slabs reinforced with CBRP rebars. The hybridization of CFRP and BFRP rebars offers a promising alternative for enhancing concrete structure strength while reducing the carbon footprint associated with traditional reinforcement materials. The versatility of CBRP rebars allows customization based on specific structural requirements. Based on the Abaqus numerical analysis and its alignment with experimental data, the study recommends optimizing the CFRP-BFRP ratio to meet structural demands, evaluating long-term performance, and exploring environmental benefits associated with sustainable reinforcement materials. This research advances knowledge in the hybrid fiber-reinforced polymers field, offering insights for engineers and researchers seeking innovative solutions for concrete structure enhancement using Abaqus as a powerful analytical tool.

Performance of doubly reinforced concrete beams with GFRP bars

Abstract The study focused on examining the behavior of six concrete beams that were reinforced with glass fiber-reinforced polymer (GFRP) bars to evaluate their performance in terms of their load-carrying capacity, deflection, and other mechanical properties. The experimental investigation would provide insights into the feasibility and effectiveness of GFRP bars as an alternative to traditional reinforcement materials like steel bars in concrete structures. The GFRP bars were used in both the longitudinal and transverse directions. Each beam in the study shared the following specifications: an overall length of 2,400 mm, a clear span of 2,100 mm, and a rectangular cross-section measuring 300 mm in width and 250 mm in depth. To apply loads for testing, two-point static loads were placed at the middle third of the beam’s span, creating a shear span of 700 mm in length. The beams were categorized into three groups depending on the GFRP longitudinal reinforcement ratio in the tension and compression zones of the section. GFRP bars with a diameter of 15 mm were employed as longitudinal reinforcement, while closed GFRP stirrups with a diameter of 8 mm at 100 mm were utilized as transverse reinforcement throughout the structural element. Test results have indicated that the ultimate load capacity of doubly GFRP-reinforced concrete beams varies compared to singly GFRP-reinforced beams. The range of variation observed is between an increase of 8% and a decrease of 4%. Accordingly, the contribution of the GFRP bars in the compression zone is insignificant and could be ignored in design calculations. It was observed that the loading level at which crack spacing stabilized ranged between 31.3 and 87% of the experimental failure load. It seems that the crack spacing decreased with the increase in the reinforcement ratio.