FRP-reinforced Concrete Beams Research Articles

Feasibility Study on the Effect of FRP Shear Reinforcements on the Behaviour of FRP-Reinforced Concrete Deep Beams

Unlike steel reinforcements in concrete, Fiber Reinforced Polymer (FRP) materials are light and free from corrosion. Therefore, FRP materials are increasingly being used in structural engineering as a replacement for steel reinforcements. While the use of FRP bars as longitudinal reinforcements in concrete deep beams has been studied somewhat widely, their use and effectiveness as web reinforcements are not well studied. In this study, the effect of the FRP web reinforcements on the behaviour and strength of FRP-reinforced concrete deep beams were investigated in an experimental study. Four glass fiber-reinforced concrete (RC) simply supported deep beam specimens were tested under a concentrated load with different shear span-to-depth ratios and web reinforcement ratios. The behaviour of the deep beams was described in terms of load–deflection behaviour, crack developments, strain in FRP reinforcements, and failure modes. The experimental investigation emphasized the significance of web reinforcements in determining the reinforced concrete deep beam behaviour, such as mid-span deflection, crack breadth, failure modes, and ultimate strengths. Furthermore, to predict the behavior of deep beams, numerical Finite Element models using Abaqus software were created. The present test results were compared to those predicted using the Finite Element models. This investigation shows that web reinforcement is quite important for FRP-RC deep beams to achieve a robust behaviour by enhancing its capacity and deformability.

Performance of Compression Yielded FRP-Reinforced Concrete Beams with T Sections

Concrete structures reinforced with fiber-reinforced polymer (FRP) reinforcement have been increasingly applied in civil engineering construction to improve several structural performances of bridges and buildings. However, the major weakness of these structures is that they typically suffer from poor ductility and brittle failure. An innovative mechanism called compression yielding (CY) has recently been proposed to enhance the ductility of FRP-reinforced concrete beams. In this study, the moment capacity and ductility of CY beams with T sections are analyzed based on a layered approach. The key variables in the design of a CY beam with a T section are identified and their importance is comprehensively investigated based on the elementary effects method and a parametric study. It is found that the ductility performance can be effectively improved by using a CY material with postmodulus ratio close to 0, and the moment capacity can be enhanced significantly by increasing the CY material strength. The most effective measure for simultaneously improving the moment capacity and ductility performance is to increase the CY block height.

Shear Strength Prediction of Slender Concrete Beams Reinforced with FRP Rebar Using Data-Driven Machine Learning Algorithms

Estimating the shear strength of a fiber-reinforced polymer (FRP)–reinforced-concrete (RC) beam is a complex task that depends on multiple design variables. The use of FRP bars has emerged as a promising alternative to diminish the corrosion problems that are associated with steel reinforcement in adverse environments; however, an accurate and reliable method of shear strength prediction is needed to ensure the economical use of materials and robust designs. Several optimized design equations are available in the literature; however, when utilizing these equations a substantial difference is observed between the predicted outcome (Vpred) and the experimental shear strength (Vexp) result. Therefore, this paper presented a novel approach toward implementing machine learning (ML) algorithms to accurately estimate the shear strength of FRP–RC beams. A large database that consisted of 302 shear test results on FRP-reinforced slender concrete beams without stirrup was collected from the literature to formulate the most efficient prediction model. The performance of each ML algorithm model was compared with the existing design provisions and models. The model interpretation was performed through feature importance analysis to explain the model output compared with a black box. The proposed data-driven ML models demonstrated a high level of accuracy and excellent performance and were superior to the existing shear strength models. In addition, a simple graphical user interface (GUI) was developed to aid practicing engineers when estimating shear strength without the need for complicated design procedures.

Shear and flexure of FRP-reinforced concrete beams and slabs – A review

The article reviews structural response of flexural members reinforced with fiber-reinforced polymer (FRP) as a sustainable alternative to conventional carbon steel. It overviews experimental studies and code-based models that characterize shear and flexural response of concrete beams and slabs reinforced with FRP bars. Discussion on lower resistance to fire being key limitation of FRP bars is also included. The most commonly used fibers to manufacture FRP bars include carbon, glass, basalt, aramid, or combinations of these materials. Consequently, each material plays its role in contributing to shear and flexure differently. Basalt FRP are characterized by higher tensile capacity and resistance to corrosion, carbon FRP bars can withstand exposure to harsh environments; glass FRP bars possess lower modulus of elasticity that may cause large deflection and wider cracks. FRP bars are generally preferred over carbon steel for use in concrete elements where exposure to chlorides or chemicals is expected, or places where non-magnetic properties are essential. Beams and slabs reinforced with FRP bars are likely to undergo larger deflections due to lower stiffness of FRP bars. The dominant failure mode of FRP-reinforced concrete flexural members is discussed in comparison to conventional steel-reinforced members. The article identifies increasing interest and research in using green concrete reinforced with FRP bars that helps to make construction serviceable and sustainable. However, premature de-bonding of bars remains a concern with more research needed to avoid deterioration of mechanical properties by underutilization of materials. Although concrete is practically fire-resistant, FRP is likely to undergo damage of resin due to the presence of combustible organic polymers. The bulk of research implies that the thermal instability of the resin in FRP reinforcing bars is not a disadvantage that outweighs the possible benefits of its usage in the construction industry.

Firefly Algorithm-Based Artificial Neural Network to Predict the Shear Strength in FRP-Reinforced Concrete Beams

The shear strength of fiber-reinforced polymer (FRP) reinforced concrete beams is often given a large safety margin by current construction requirements. Six characteristics are utilized as inputs to compute the shear strength of FRP-reinforced concrete beams. This study uses 198 samples from the literature to predict the shear strength of 139 training samples and 59 testing samples. Additionally, the ANN structure is optimized with the firefly algorithm. The FA-ANN model is also compared to ACI-440, CSA-S806, and BISE-99 codes, and the optimized model by Nehdi et al. Findings show that regarding the shear strength of FRP-reinforced concrete beams, the firefly algorithm-optimized model performs better than the other four models. Concerning accuracy, the coefficient of correlation, R2, was calculated as 0.961, while the average absolute error (AAE) is 0.22 for the shear strength of FRP-reinforced beams.

Experimental and numerical study on the effect of fibers on shear strength of basalt FRP-reinforced concrete beams without stirrup

Experimental and numerical study on the effect of fibers on shear strength of basalt FRP-reinforced concrete beams without stirrup

Data-Driven Shear Strength Prediction of FRP-Reinforced Concrete Beams without Stirrups Based on Machine Learning Methods

Due to the intrinsic complexity, there has been no widely accepted mechanics-based estimation model of the shear performance of Fiber-Reinforced Polymer (FRP)-reinforced concrete beams. Capitalizing on a large amount of previous experimental data, data-driven machine learning (ML) models could be potentially suitable for addressing this problem. In this paper, four existing shear design provisions are reviewed and four typical ML models are analyzed. The accuracy of codified methods and ML models are compared and analyzed based on our established extensive database of FRP-reinforced concrete beams with rectangular cross sections. A series of artificially selected features considering the shear-carrying mechanisms of FRP-reinforced beams are incorporated into the proposed ML models to show their influence on the model validity. Bayesian optimization is utilized to automatically tune the hyperparameters of different ML models. Compared to the most satisfying codified predictions from CSA S806, the best ML model, XGBoost, can provide more accurate and consistent predictions for the database, with R2 enhanced by 15% and the MAE and RMSE reduced by 59% and 52%, respectively. With the selected features based on domain knowledge, the performance of ML models is further enhanced, shown by the most important features being the added ones. With outstanding performance on a large database and singular test, the ML approaches have great potential in guiding the shear design of FRP-reinforced concrete.

Numerical Simulation and Flexural Capacity of Hybrid-Bonded FRP Reinforced Concrete Beam

Numerical Simulation and Flexural Capacity of Hybrid-Bonded FRP Reinforced Concrete Beam

Design of concrete beam reinforced with GFRP bars as per ACI codal provisions

This document provides design principles for concrete beams reinforced with glass fiber reinforced polymer (GFRP) bars per the ACI 440.1R-15 regulation. One of the main advantages of using glass fiber reinforced polymer rods instead of traditional steel reinforced rods is their lighter weight and higher corrosion resistance. However, the bending failure mode of FRP reinforced concrete (FRP-RC) beams is brittle rather than ductile because the elasticity of fiber reinforced polymer (FRP) bars is linear until failure and the elongation at break is small. For FRP-RC elements, concrete crushing compression failure, which gives various warnings before failure, is the preferred failure mode. In other words, unlike the usual design practice for reinforced concrete (steel-RC) beams, for FRP-RC beams, an over-reinforced structure is preferable to an under-reinforced structure. In addition, since the FRP RC member has low rigidity of the FRP rod, it bends more and cracks larger than the steel RC member. These factors limit the field of application of FRP. Here is a design example of a rectangular beam with tension reinforcement according to ACI regulations.

Effect of stirrup ratio on impact response of BFRP-reinforced concrete beams under different energy levels

Fiber-reinforced-polymer (FRP) reinforced concrete structures are gradually applied in building, bridge, dam engineering, etc. Compared with the extensively-studied static mechanical properties, the research on the impact resistance of FRP-reinforced concrete beams is minimal. This study established and validated the 3D modeling approach of Basalt FRP (BFRP) reinforced concrete beams with different stirrup ratios. Numerical simulations were performed under different impact energy. The effects of the stirrup ratio and impact energy on the mechanical response of concrete beams were presented and analyzed. It can be concluded that the increasing stirrup ratio can restrict the development of bending-shear cracks and thus preclude shear plug failure. Under the given impact energy, as the stirrup ratio increases, the midspan peak and residual displacement are reduced. More stirrups are activated while stirrup peak strains decrease in the shear region. The increasing stirrup ratio enhances the integrity and overall rigidity of the beams, which is more effective at higher velocities. Moreover, a nominal equal-damage analysis on the beams was performed according to energy dissipation, which provides a reference for in-depth research and design. Finally, considering the relationship between the static load-carrying capacity and dynamic input energy of the beam, a semi-empirical simplified method for predicting the peak impact deflection of concrete beams was suggested and initially verified. The simplified prediction method provides new insights for impact mechanical response investigation and engineering structure design for BFRP-reinforced concrete beams.

Fracture characteristics analysis of FRP-reinforced concrete based on acoustic emission

To study the acoustic emission (AE) characteristics of fibre-reinforced polymer (FRP)-reinforced concrete during the fracture process, three-point bending tests of FRP-reinforced concrete beams with cracks were carried out. The AE characteristics of FRP-reinforced concrete beams with different bonding lengths and different bonding layers were analysed based on the AE parameters, and the effect of the fracture parameters was studied. The test results reveal that the crack initiation load and peak load of single-layer FRP-reinforced concrete beams first increase and then decrease with the increase of bonding length. The failure mode of multi-layer FRP-reinforced concrete beams is a kind of dangerous brittle failure, which easily forms the ‘super-reinforcement failure’ mode. The ductility of single-layer FRP-reinforced concrete beams with a bonding length of 25 cm is optimal, which can effectively delay crack propagation. By combining the load–time curves, AE ringing count–time curves and AE energy–time curves, the time points of crack initiation load and instability load can be accurately determined. The analysis of RA–AF (ratio of ringing count to duration–ratio of rise time to maximum amplitude) and clearance factor index can be used to evaluate the damage state of FRP-reinforced concrete beams.

Prediction of the FRP reinforced concrete beam shear capacity by using ELM-CRFOA

In reinforced concrete structures, the utilization of composite rebar has been increased by considering their high corrosion resistance, anti-magnetic properties, and significant tensile strength. According to the lower elasticity modulus of composite rebar in comparison with steel rebar, concrete beams reinforced including composite rebar possess a relatively lower shear strength by comparing with steel rebar. In addition, in concrete beam, reinforced shear failure by composite rebar is commonly brittle and requires precise performance prediction of the members. Thus, the reinforced concrete beams' shear strength by composite rebar is predicted utilizing an Extreme Learning Machine network based on Chaos Red Fox Optimization Algorithm (ELM-CRFOA) according to a wide range of data. The most important parameters, which are considered in this investigation, are the web width, beam effective depth, the strength of concrete compressive, the ratio of the shear span to depth, FRP longitudinal bars elasticity modulus, and ratio of the longitudinal reinforcement. This method's precision has been proved by having a comparison among the model predictions and the accumulated data and available shear design equations. According to the study outcomes, the presented model has precise outcomes in computing the concrete beams' shear strength in comparison with other existing relations. For assessing input parameters' impact on the FRP-reinforced concrete beams' shear strength, a sensitivity analysis is executed.

Machine learning-driven evaluation and optimisation of compression yielded FRP-reinforced concrete beam with T section

Fibre reinforced polymer (FRP)-reinforced concrete beams usually encounter brittle failure due to the linear behaviour of FRP. To solve this issue, compression yielding (CY) concept was proposed recently to improve the ductility of FRP-reinforced concrete beams. However, because of the complexity of the compression yielding mechanism, the calculation of flexural capacity and ductility of FRP-reinforced concrete beam with CY block (CY beam) is challenging, especially for the CY beam with T section due to the lack of closed form solution. In this study, an integrated model is proposed based on machine learning method to evaluate the moment capacity and ductility of the CY beam with T section. To improve the prediction accuracy of the artificial neural network model for moment capacity, different activation functions are tested. The section effectiveness of CY beam with T section is evaluated using the proposed support vector machine model, which combines different kernel functions. Gaussian process regression is then employed to predict the ductility of CY beam with T section. It demonstrates that the developed integrated model can produce highly accurate predictions. In addition, a genetic algorithm (GA) is developed for identifying optimal CY beam section design solutions. Finally, the robustness of the model in the optimisation design for the CY beams with rectangular section and T section is demonstrated by using two numerical examples.

An Extreme Gradient Boosting approach to estimate the shear strength of FRP reinforced concrete beams

Although fiber-reinforced polymer (FRP) bars have gained much attention recently, their material characteristics, particularly the elastic modulus, are markedly different from conventional reinforcement. This explains why classical calculation models cannot correctly capture the behaviors of FRP-reinforced concrete (RC) beams in structural engineering. As a novel approach, this study aims to construct a machine learning (ML) model for predicting the shear strength (SS) of FRP-RC beams with and without stirrups. An Extreme Gradient Boosting (XGB) model is selected thanks to its effectiveness and robustness in tackling many complicated problems. An experimental database encompassing 453 examples is utilized, with input variables covering the FRP-RC beams’ geometry, the mechanical properties of concrete, and two reinforcement components. The selection of hyperparameters of XGB model is first conducted, followed by a prediction performance assessment using common statistical measures. Additionally, this study conducts feature importance analysis and employs SHAP values technique to discover the inputs versus output relationship, as well as to identify the most significant features affecting the SS of FRP-RC beams. The outcome of this research, which lies in developing a reliable and accurate ML model that can handle both FRP-RC beams with and without stirrups, may favor the application of ML models to various real-world engineering problems.

Experimental and Theoretical Study on Flexural Behavior of GFRP- and CFRP-Reinforced Concrete Beams after High-Temperature Exposure.

In this study, in order to study the flexural behavior of fiber-reinforced polymer (FRP) bars with reinforced concrete beams under static loads after high-temperature exposure, seven pieces of FRP-reinforced concrete beams were subjected to static bending tests and calculation model derivations. Four-point bending tests were carried out on FRP-reinforced concrete beams after high temperature treatment. The effects of high temperature and types of FRP bars on the cracking load, crack development, deflection and ultimate capacity, and failure mode of concrete beams were investigated. The test results show that the maximum crack width, deflection, and ultimate bearing capacity of GFRP- and CFRP-reinforced concrete beams decrease obviously with a rise in high temperature. After the exposure of 400 °C for 2 h, compared with the behavior of concrete beams at room temperature, the maximum crack width of GFRP and CFRP-reinforced concrete beams increased by 42.9% and 41.7%, respectively, the deflection increases by 103.6% and 22.0%, and the ultimate bearing capacity decrease by 11.9% and 3.9%. Meanwhile, through the analysis of the existing research results and test results, the calculation models for the maximum crack width, deflection, and residual ultimate capacity of FRP-reinforced concrete beams after exposure of high temperature were proposed. For FRP-reinforced concrete beams after high-temperature exposure, the errors between the measured maximum crack width, stiffness, residual bearing capacity, and their corresponding calculated values using the model were mostly less than 10%. The calculated value using the proposed model in this research is in good agreement with the measured value. The mechanical properties of FRP-reinforced high-strength concrete structures after high-temperature exposure can be preliminarily predicted, which provides a new theoretical basis for the application of FRP-reinforced concrete structures.

Design of concrete beams reinforced and unbonded prestressed with FRP bars based on serviceability requirements

The serviceability requirements for concrete beams reinforced with fiber-reinforced polymer (FRP) bars usually entail repeated check of deflection and crack width in design process. In this work, a contribution to the development of the design recommendations at serviceability limit state for beams reinforced and unbonded prestressed with FRP bars is presented. To facilitate the design, equations of internal force arm coefficient, nonuniformity coefficient of strain, and position amplification factor were empirically modified using beam test database with the principle of conservation. Accordingly, simplified deflection and crack width models for FRP-reinforced concrete (FRP–RC) beams and unbonded FRP prestressed concrete (FRP–UPC) ones were proposed. Moreover, for FRP–RC beams, safety factors were introduced based on statistical analysis of experimental data. Considering the serviceability-related requirements, reinforcement amount limits and design procedures for both types of beams were provided. Furthermore, to evaluate the cracking behavior of FRP–RC beams, a cracking resistance coefficient was defined. Parametric analysis revealed that the reinforcing layer number of the beam corresponding to the highest cracking resistance coefficient showed a general uptrend with the increase of the beam height. On this basis, a rational method to determine the optimal number of reinforcing layers was proposed.

AI-based shear capacity of FRP-reinforced concrete deep beams without stirrups

The presented work utilizes Artificial Intelligence (AI) algorithms, to model and interpret the behavior of the fiber reinforced polymer (FRP)-reinforced concrete deep beams without stirrups. This is done by first running an extensive nonlinear finite element analysis (NLFEA) investigation, spanning across the practical ranges of the different input parameters. The FEA modeling is meticulously validated against published experimental results. A total of 93 different models representing a multitude of possible FRP-reinforced deep beam designs are rigorously analyzed. The results are then utilized in building an AI-model that describes the shear capacity for FRP-reinforced deep beams. The study investigates the effect of several factors on the shear capacity and identifies the vital parameters to be used for further model development. Additionally, the developed AI-model is benchmarked against several design standards for blind predictions on new unseen data and design codes, namely: the EC, ACI 440.1R-15, and the modified ACI 440.1R-15 (for size effect). The AI-model demonstrated superior generalization on the blind prediction dataset in comparison to the design codes.

A Machine Learning Model for Torsion Strength of Externally Bonded FRP-Reinforced Concrete Beams.

Strengthening of reinforced concrete (RC) beams subjected to significant torsion is an ongoing area of research. In addition, fiber-reinforced polymer (FRP) is the most popular choice as a strengthening material due to its superior properties. Moreover, machine learning models have successfully modeled complex behavior affected by many parameters. This study will introduce a machine learning model for calculating the ultimate torsion strength of concrete beams strengthened using externally bonded (EB) FRP. An experimental dataset from published literature was collected. Available models were outlined. Several machine learning models were developed and evaluated. The best model was the wide neural network, which had the most accurate results with a coefficient of determination, root mean square error, mean average error, an average safety factor, and coefficient of variation values of 0.93, 1.66, 0.98, 1.11, and 45%. It was selected and further compared with the models from the existing literature. The model showed an improved agreement and consistency with the experimental results compared to the available models from the literature. In addition, the effect of each parameter on the strength was identified and discussed. The most dominant input parameter is effective depth, followed by FRP-reinforcement ratio and strengthening scheme, while fiber orientation has proven to have the least effect on the prediction output accuracy.

Ensemble Tree-Based Approach towards Flexural Strength Prediction of FRP Reinforced Concrete Beams.

Due to rise in infrastructure development and demand for seawater and sea sand concrete, fiber-reinforced polymer (FRP) rebars are widely used in the construction industry. Flexural strength is an important component of reinforced concrete structural design. Therefore, this research focuses on estimating the flexural capacity of FRP-reinforced concrete beams using novel artificial intelligence (AI) decision tree (DT) and gradient boosting tree (GBT) approaches. For this purpose, six input parameters, namely the area of bottom flexural reinforcement, depth of the beam, width of the beam, concrete compressive strength, the elastic modulus of FRP rebar, and the tensile strength of rebar at failure, are considered to predict the moment bearing capacity of the beam under bending loads. The models were trained using 60% of the database and were validated first-hand on the remaining 40% database employing the correlation coefficient (R), error indices namely, mean absolute error, root mean square error (MAE, RMSE) and slope of the regression line between observed and predicted results. The developed models were further validated using sensitivity and parametric analysis. Both models revealed comparable performance; however, based on the comparison of the slope of the validation data (0.83 for GBT model against 0.75 for the DT model) and higher R for the validation phase in case of the GBT model in comparison to the DT, the GBT model can be considered more accurate and robust. The sensitivity analysis yielded depth of the beam as the most influential parameter in contributing flexural strength of the beam, followed by the area of flexural reinforcement. The developed GBT model surpasses the existing gene expression programming (GEP) model in terms of accuracy; however, the current American Concrete Institute (ACI) model equations are more reliable than AI models in predicting the flexural strength of FRP-reinforced concrete beams.

Experimental Study of GFRP Reinforced Concrete Beams With U-Shaped CFRP Grid-Reinforced ECC Stay-in-Place Formwork

Corrosion is a major concern for structures under the harsh environment. In this paper, ECC was applied as an alternative to concrete in tension zones, which was combine with FRP bars to develop a new type of composite beam with excellent durability. The flexural performance of glass fiber-reinforced polymer (GFRP)-reinforced concrete beams with U-shaped CFRP grid-reinforced ECC formwork was investigated. Five beams were designed, including one reference beam with full concrete, one composite beam with a coupon-shaped ECC permanent formwork at the bottom, and three composite beams with the proposed formwork. The effects of the application of the U-shaped ECC formwork, the formwork types, the application of CFRP grids, and the strength of the ECC were investigated. The test results showed that ECCs were an ideal material to improve the flexural behavior of FRP-reinforced concrete beams. The proposed formwork had a positive effect on crack resistance at the serviceability limit and substantially improved the cracking load by 78.57–85.71% and the ultimate load by 5.29–15.71% compared with those of the reference beam. Moreover, the proposed formwork improved the shear stiffness of the composite beam. The addition of CFRP grids improved the tensile strength of the ECC formwork and consequently contributed to the improvement in the load capacity of the composite beams.