FRP-reinforced Concrete Research Articles

Development of Prediction Models for Shear Strength of FRP Reinforced Fibrous Concrete Beams without Stirrups Using ANN and Nonlinear Regression

ABSTRACT The shear behaviour of fibre-reinforced polymer reinforced concrete (FRP-RC) beams without web reinforcement suffers from low strength, low stiffness, more brittleness and wide and quick propagated cracks. Fortunately, the addition of various types of fibres could improve most of these weaknesses. On the other hand, the shear strength prediction of FRP-RC beams with various types of fibres is one of the most complex cases in structural engineering applications. Developing generalised, precise and consistent prediction models are necessary and very limited. This paper investigates the impacts of various types of fibres on shear strength and presents proposing four new prediction models, utilising artificial neural networks and empirical nonlinear regression analysis, and modifying the combination of available models based on a collected database of 49 shear test results of FRP-RC members with various types of fibres. The comparison of the developed models with the available equations from the literature indicates that the developed models yielded excellent performance, great efficiency and a high level of accuracy over all other existing models. Additionally, the parametric study confirmed that all the developed models have great abilities to accurately predict the actual response of each parameter, in spite of its complexity, on the shear strength of FRP-reinforced fibrous concrete beams without stirrups.

Flexural property and design method evaluation of BFRP and steel bars hybrid reinforced concrete beams

AbstractFiber reinforced polymer (FRP) bars have been widely used as a replacement for steel bars in civil engineering due to their high strength and superior durability. However, compared to steel reinforced concrete (RC) beams, FRP RC beams have insufficient ductility and are more expensive. Therefore, a hybrid beam reinforced with steel and FRP bars has been proposed recently. In this study, the flexural properties of steel bar RC beam, basalt FRP (BFRP) bar RC beam, and BFRP steel bars hybrid RC beams were studied based on numerical simulation. The effect of As/Af on the stress of longitudinal reinforcements, load–midspan curves, ductility, and overall performance of the hybrid RC beams is analyzed. Results indicated that the hybrid RC beams exhibited a second‐order stiffness after the steel bars yielded owing to the contribution of the BFRP bars. As As/Af increases, the ductility of the hybrid RC beam and the strength utilization of BFRP bars increase, and a preferable ductility and overall performance can be achieved when As/Af was close to 1.0. The theoretical calculation results of hybrid RC beams based on ACI, CSA, and GB codes are compared with the simulation results, and the accuracy of the ultimate flexural strength of the prediction results is verified. In addition, equations for predicting the deflection of hybrid RC beams are proposed, and they exhibit higher prediction accuracy compared to the prediction models based on the ACI code.

Metaheuristic Optimization of Random Forest for Predicting Punch Shear Strength of FRP-Reinforced Concrete Beams.

Predicting the punching shear strength (PSS) of fiber-reinforced polymer reinforced concrete (FRP-RC) beams is a critical task in the design and assessment of reinforced concrete structures. This study utilized three meta-heuristic optimization algorithms, namely ant lion optimizer (ALO), moth flame optimizer (MFO), and salp swarm algorithm (SSA), to select the optimal hyperparameters of the random forest (RF) model for predicting the punching shear strength (PSS) of FRP-RC beams. Seven features of FRP-RC beams were considered as inputs parameters, including types of column section (TCS), cross-sectional area of the column (CAC), slab's effective depth (SED), span-depth ratio (SDR), compressive strength of concrete (CSC), yield strength of reinforcement (YSR), and reinforcement ratio (RR). The results indicate that the ALO-RF model with a population size of 100 has the best prediction performance among all models, with MAE of 25.0525, MAPE of 6.5696, R2 of 0.9820, and RMSE of 59.9677 in the training phase, and MAE of 52.5601, MAPE of 15.5083, R2 of 0.941, and RMSE of 101.6494 in the testing phase. The slab's effective depth (SED) has the largest contribution to predicting the PSS, which means that adjusting SED can effectively control the PSS. Furthermore, the hybrid machine learning model optimized by metaheuristic algorithms outperforms traditional models in terms of prediction accuracy and error control.

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.

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.

Experimental and numerical investigations of the effects of various tensile reinforcement types on the structural behavior of concrete bridge deck slabs

Fiber-reinforced polymers (FRPs) have attracted attention owing to their ability to solve steel corrosion problems. However, FRP-reinforced concrete (RC) deck slabs were found to fail in a brittle shear manner due to the low stiffness of the FRP bars. In contrast, steel-RC deck slabs failed in ductile flexural failure by yielding of steel reinforcement. This study attempts to avoid such brittle failure in FRP-RC slabs by using hybrid steel/FRP bars that can provide serviceability, high strength, and durability. Seven full-scale reinforced concrete bridge deck slabs were cast and tested under four-point loading up to failure. The slabs were 3100mm length × 1000mm width × 200mm thickness. The investigated parameters were the reinforcement type and ratio and different patterns of the hybrid system. Subsequently, a finite element model was constructed, and the model results were compared to the experimental test results. Moreover, a numerical study was carried out by studying the different parameters affecting the shear behavior of RC members, such as the reinforcement ratio, FRP-to-steel ratio and wide range of the shear span-to-depth ratio. Based on the test results, Hybrid-RC slabs showed flexural-shear or compression-shear failure with ample warning than that reinforced with pure BFRP bars. Increasing the reinforcement ratio enhanced the overall slab performance at serviceability limit state. Furthermore, the numerical study indicated that the FRP-to-steel ratio should be Af/As ≤ 1.0 for achieving favorable ductile failure.

Bond durability between anchored GFRP bar and seawater concrete under offshore environmental conditions

The lower bond strength of FRP bars to concrete compared to steel bars has remained an unsolved barrier to the widespread use of FRP-reinforced concrete under extreme loading. Additionally, the degradation of the bond between FRP reinforcement and concretes in aggressive environments adds to the existing concern. In this study, an innovative anchorage system comprised of polypropylene pipe was used to strengthen the bond between seawater concrete and GFRP bars after 250 days of exposure to offshore environmental conditions. As material factors, two types of GFRP bars (sand-coated and ribbed) and two types of concrete (normal and seawater concrete) were evaluated. Four distinct environmental conditions were used to assess the samples: (i) ambient environment (control), (ii) tap water, (iii) seawater, and (iv) wet-dry cycles in seawater. According to the findings of the direct pull-out tests, the suggested anchor system strengthens the bond and shifts the failure mode from bond failure to bar rupture. Additionally, after exposure to 250 days of seawater wet-dry cycles, GFRP-reinforced seawater concrete lost 5% of its maximum bond strength (developed bar tensile stress). All other samples exposed to different environmental conditions either increased or decreased in bond strength by less than 5% after 250 days, compared to the control samples.

FRP-Reinforced/Strengthened Concrete: State-of-the-Art Review on Durability and Mechanical Effects

Fiber-reinforced polymer (FRP) composites have gained increasing recognition and application in the field of civil engineering in recent decades due to their notable mechanical properties and chemical resistance. However, FRP composites may also be affected by harsh environmental conditions (e.g., water, alkaline solutions, saline solutions, elevated temperature) and exhibit mechanical phenomena (e.g., creep rupture, fatigue, shrinkage) that could affect the performance of the FRP reinforced/strengthened concrete (FRP-RSC) elements. This paper presents the current state-of-the-art on the key environmental and mechanical conditions affecting the durability and mechanical properties of the main FRP composites used in reinforced concrete (RC) structures (i.e., Glass/vinyl-ester FRP bars and Carbon/epoxy FRP fabrics for internal and external application, respectively). The most likely sources and their effects on the physical/mechanical properties of FRP composites are highlighted herein. In general, no more than 20% tensile strength was reported in the literature for the different exposures without combined effects. Additionally, some provisions for the serviceability design of FRP-RSC elements (e.g., environmental factors, creep reduction factor) are examined and commented upon to understand the implications of the durability and mechanical properties. Furthermore, the differences in serviceability criteria for FRP and steel RC elements are highlighted. Through familiarity with their behavior and effects on enhancing the long-term performance of RSC elements, it is expected that the results of this study will help in the proper use of FRP materials for concrete structures.

Eccentric compression behavior of Steel-FRP composite bars RC columns under coupling action of chloride corrosion and load

In order to investigate the eccentric compression behaviors of steel-FRP composite bar (SFCB) reinforced concrete (RC) columns subjected to chloride corrosion, the mechanical experiments of chloride corroded SFCBs and SFCBs RC eccentric compression columns were conducted. The effect of reinforcement type and ratio, eccentricity, slenderness, stress level and corrosion duration on bearing capacity, deformation, crack and failure pattern were investigated. The results showed that the strength retention ratio of reinforcement decreases with the increase of corrosion duration, the ultimate strengths of steel rebar, SFCB and FRP rebar decreased by 12.2%, 9.9% and 3.6%, respectively, when compared with those of uncorroded counterparts. With the increase of steel content of reinforcement, the load bearing capacity of eccentric compression RC column increases while the deformation decreases gradually. The load bearing capacity of corroded steel, SFCB and FRP RC columns maximally decreased by 16.6%, 12.4% and 7.2%, respectively, when compared with those of uncorroded counterparts. Based on the simplified materials constitutive relations and reasonable basic assumptions, formulae for discriminate failure mode, moment magnification factor and bearing capacity were developed. The predicted failure pattern, moment magnification factor and bearing capacity are in good agreement with the test results, confirming the validity of the proposed formulae, the results can be used as a reference for engineering application.

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.

Sensitivity analysis and genetic algorithm-based shear capacity model for basalt FRC one-way slabs reinforced with BFRP bars

Fiber-reinforced polymer (FRP) composites are increasingly used in concrete structures owing to their superior corrosion resistance. However, FRP-reinforced concrete (RC) structures exhibit less ductile response compared to steel RC structures. Recently, the use of basalt fiber reinforced concrete (BFRC) reinforced with BFRP bars was investigated to achieve a reasonable level of ductility in BFRC-BFRP one-way slabs. The shear behavior of such a slab depends on different design parameters. This paper aims to identify the impact of each design parameter on the shear behavior of BFRC-BFRP one-way slabs using a fractional factorial design of experiment (DOE). A 3D finite element model was first developed and validated against available experimental results. The developed model is then used to conduct a sensitivity analysis considering five significant factors that influence the shear behavior of BFRC-BFRP one-way slabs. These factors are the longitudinal reinforcement ratio, shear span-to-depth ratio, effective depth, concrete compressive strength, and volume fraction of BMF. Finally, a design equation that can predict the shear capacity of one-way BFRC-BFRP slabs was proposed based on genetic algorithm. The proposed model showed the best prediction accuracy compared to the available design codes and guidelines with a mean Vpred/Vexp of 0.97 and a COV of 17.91%.

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.

Mechanical Properties on Various FRP-Reinforced Concrete in Cold Regions

The evaluation of frost resistance varies with different reinforcement methods, but it is a hot research topic for concrete reinforced with Fiber-Reinforced plastic (FRP). Freezing and thawing tests of FRP-reinforced concrete prisms and cylinders are presented to simulate beams and piers of buildings in cold climates. To evaluate the specimens’ frost resistance, tests with various reinforcement techniques, morphological analysis, weight tests, and relative dynamic modulus of elasticity tests were used. Examined also were the variations in stress–strain curves for axial compression tests and load–displacement curves for bending tests following various freeze–thaw cycles. The findings indicated that after 100 freeze–thaw cycles, the weight of unreinforced concrete cylinders decreased by 9.7%, and its compressive strength decreased by 27.6%. On the other hand, CFRP-reinforced concrete cylinders (Carbon-Fiber-Reinforced Plastics) and GFRP (Glass-Fiber-Reinforced Plastics) gained 1.1% and 1.58% in weight, respectively, while the compressive strength decreased by 7.4% and 8%. After 100 freeze–thaw cycles, the weights of concrete prisms with reinforcement, without reinforcement, and with CFRP reinforcement decreased by 12.13%, 8.7%, and 9.6%, respectively, and their bending strength was reduced by 20%, 42%, and 53%, respectively. The frost resistance of the two FRP-reinforced concrete types had significant differences under freeze–thaw cycles because the prismatic specimens were not fully wrapped with FRP materials. Finally, finite element software ABAQUS was used to simulate the freeze–thaw cycle test of the two specimens. Calculated values were compared to experimental results for the load–displacement curve and the axial stress–strain curve under bending load. The comparison of peak displacement produced a maximum error of 8.6%, and the FRP-reinforced concrete model validity was verified.

Study on Fuzzy Reliability of FRP-Reinforced Concrete Under Serviceability Limit State in Chinese Code

Study on Fuzzy Reliability of FRP-Reinforced Concrete Under Serviceability Limit State in Chinese Code

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.

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.

FAI: Fast, accurate, and intelligent approach and prediction tool for flexural capacity of FRP-RC beams based on super-learner machine learning model

Fiber-reinforced polymer (FRP) composites have recently been considered in the field of structural engineering as one of the best alternatives to conventional steel reinforcement due to their high tensile strength, lightweight, cost-effectiveness, and superior corrosion resistance. However, the variation in FRP physical and mechanical characteristics among the different FRP types and manufacturers makes it difficult to predict the strength of FRP-reinforced concrete (RC) members. For that reason, an efficient prediction tool was developed for a fast, accurate, and intelligent (FAI) prediction of the flexural capacity of FRP-RC beams based on the result of an optimized super-learner machine learning (ML) model. A database of the experimental results on the flexural strength of FRP-RC beams was compiled and randomly split into 80% training and 20% test sets. Six factors were considered in the model; namely, width and effective depth of the beam, concrete compressive strength, FRP flexural reinforcement ratio, FRP modulus of elasticity, and FRP ultimate tensile strength. Grid search is combined with a 10-fold cross-validation to optimize the hyperparameters of the ML models. The prediction capability of the proposed super-learner ML model was benchmarked against boosting- and tree-based ML models, such as classification and regression trees, adaptive boosting, gradient boosted decision trees, and extreme gradient boosting. Moreover, a comparison with the existing code and guideline equations showed that the proposed super-learner ML model provided the most desirable prediction of the flexural capacity of FRP-RC beams.

Predictive models of behavior and capacity of FRP reinforced concrete columns

This paper proposes a new model for predicting the axial capacity and behavior of Fiber Reinforced Polymerreinforced concrete (FRP-RC) columns using a promising variant of Genetic Expression Programming (GEP). Current design codes, such as the ACI 440.1R-15 and the Canadian Code CSA S806, disregard the compressive contribution of FRP bars when used in compression members. The behavior of concentrically short FRP-RC columns has been widely investigated in the past few years; however, limited research has been dedicated to investigating the effect of load eccentricity and the slenderness ratio of FRP-RC columns. In addition, the methodologies adopted for including the effect of column slenderness remain a subject of debate, as no solid conclusions are withdrawn in this regard. In this paper, the experimental results of FRP-RC columns are gathered from the literature and used to formulate two GEP models to predict the axial capacity based on load eccentricity. The experimental data includes columns reinforced with different FRP types and subjected to concentric and eccentric axial compressive loads. In addition, the database comprises short and slender columns. The proposed GEP models are functions of concrete compressive strength, longitudinal reinforcing bars ratio, FRP bars elastic modulus, eccentricity level, and column dimensions. For the aim of comparison, a preliminary evaluation of previously suggested empirical equations/models for estimating the axial capacity of FRP-RC columns was carried out over the collected database. The proposed models showed superior accuracy in axial capacity prediction with coefficients of determination R2 equals to 0.978 and R2 equal to 0.992 for eccentric and concentric axial load, respectively. The proposed models were found to give reliable estimates of the axial capacity of columns reinforced with FRP longitudinal bars. Finally, a parametric study to evaluate the effect of each variable on the proposed models was conducted.

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.

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.