Performance of Code Equations Compared to Experimental Data for Shear Capacity of FRP-RC Beams

Current approaches for estimation of shear capacity of concrete beams reinforced with fiber-reinforced polymer (FRP) are generally based on existing semi-empirical shear design equations for steel-reinforced concrete (S-RC). These equations were primarily evaluated based on experimental data generated on concrete beams with steel reinforcement. However, FRP materials have different mechanical properties and accordingly exhibit different modes of failure than steel, making the extension of existing shear design equations for S-RC beams to cover concrete beams reinforced with FRP somehow inaccurate. Currently available methods include ACI 440-06, JSCE-97, CSA S806-02, and ISIS Canada-01. Availability of FRP reinforcement products varies in terms of capacity and modulus of elasticity, which can result in a significant change in behavior. An experimental database of 150 FRP-reinforced concrete (FRP-RC) beams was developed from published literature. Subsequently, this database was used to assess the validity of these four main existing shear design methods for FRP-RC beams. This research investigates the performance of the abovementioned design methods to estimate the nominal shear capacity, Vn of steel-free concrete beams reinforced with FRP bars. Results show that current design guidelines provide a shear strength underestimation in the case of beams without shear reinforcement and a shear strength overestimation for beams with shear reinforcement

Proposed Shear Design Equations for FRP-Reinforced Concrete Beams Based on Genetic Algorithms Approach

To calculate the shear capacity of structural members reinforced with fiber-reinforced polymer (FRP), current shear design provisions generally use slightly modified versions of existing semiempirical shear design equations initially developed for steel reinforced concrete beams. Such methods generally assume that the traditional approach of superimposing concrete contribution to shear, Vc to that of stirrups, Vs can also be used to calculate the nominal shear capacity, Vn of FRP-reinforced concrete beams provided that the axial rigidity of FRP longitudinal bars and the capacity of FRP stirrups at the bent portions are accounted for. These methods also noticeably vary in the manner they account for the effect of basic shear design parameters on shear strength. This paper presents simple yet improved equations to calculate the shear capacity of FRP-reinforced concrete beams based on the genetic algorithms approach. The performance of the proposed equations is compared to that of four commonly used shear design methods for FRP-reinforced concrete beams, namely the ACI 440, CSA S806, JSCE, and ISIS Canada. Results show that current guidelines are either inadequate or very conservative in estimating the shear strength of FRP-reinforced concrete beams. Moreover, the shear capacity of FRP-reinforced concrete beams calculated using the proposed equations is in better agreement with available experimental data than that calculated using shear equations provided by current provisions. This study also shows that the axial rigidity of FRP longitudinal bars is best represented by a cubic root function and that the contribution of FRP stirrups to shear strength is a square root function of the stirrups ultimate capacity rather than a linear function as proposed by current shear provisions

Evaluation of Shear Capacity of FRP Reinforced Concrete Beams Using Artificial Neural Networks

To calculate the shear capacity of concrete beams reinforced with fibre-reinforced polymer (FRP), current shear design provisions use slightly modified versions of existing semi-empirical shear design equations that were primarily derived from experimental data generated on concrete beams having steel reinforcement. However, FRP materials have different mechanical properties and mode of failure than steel, and extending existing shear design equations for steel reinforced beams to cover concrete beams reinforced with FRP is questionable. This paper investigates the feasibility of using artificial neural networks (ANNs) to estimate the nominal shear capacity, Vn of concrete beams reinforced with FRP bars. Experimental data on 150 FRP-reinforced beams were retrieved from published literature. The resulting database was used to evaluate the validity of several existing shear design methods for FRP reinforced beams, namely the ACI 440-03, CSA S806-02, JSCE-97, and ISIS Canada-01. The database was also used to develop an ANN model to predict the shear capacity of FRP reinforced concrete beams. Results show that current guidelines are either inadequate or very conservative in estimating the shear strength of FRP reinforced concrete beams. Based on ANN predictions, modified equations are proposed for the shear design of FRP reinforced concrete beams and proved to be more accurate than existing equations

Predicting Shear Capacity of NSC and HSC Slender Beams Using Artificial Intelligence

The use of high-strength concrete (HSC) has significantly increased over the last decade, especially in offshore structures, long-span bridges, and tall buildings. The behavior of such concrete is noticeably different from that of normal-strength concrete (NSC) due to its different microstructure and mode of failure. In particular, the shear capacity of structural members made of HSC is a concern and must be carefully evaluated. The shear fracture surface in HSC members is usually trans-granular (propagates across coarse aggregates) and is therefore smoother than that in NSC members, which reduces the effect of shear transfer mechanisms through aggregate interlock across cracks, thus reducing the ultimate shear strength. Current code provisions for shear design are mainly based on experimental results obtained on NSC members having compressive strength of up to 50MPa. The validity of such methods to calculate the shear strength of HSC members is still questionable. In this study, a new approach based on artificial neural networks (ANNs) was used to predict the shear capacity of NSC and HSC beams without shear reinforcement. Shear capacities predicted by the ANN model were compared to those of five other methods commonly used in shear investigations: the ACI method, the CSA simplified method, Response 2000, Eurocode-2, and Zsutty\u27s method. A sensitivity analysis was conducted to evaluate the ability of ANNs to capture the effect of main shear design parameters (concrete compressive strength, amount of longitudinal reinforcement, beam size, and shear span to depth ratio) on the shear capacity of reinforced NSC and HSC beams. It was found that the ANN model outperformed all other considered methods, providing more accurate results of shear capacity, and better capturing the effect of basic shear design parameters. Therefore, it offers an efficient alternative to evaluate the shear capacity of NSC and HSC members without stirrups

Predicting Effect of Stirrups on Shear Strength of Reinforced NSC and HSC Slender Beams Using Artificial Intelligence

The exact effect that each of the basic shear design parameters exerts on the shear capacity of reinforced concrete (RC) beams without shear reinforcement (Vc) is still unclear. Previous research on this subject often yielded contradictory results, especially for reinforced high-strength concrete (HSC) beams. Furthermore, by simply adding Vc and the contribution of stirrups Vs to calculate the ultimate shear capacity Vu, current shear design practice assumes that the addition of stirrups does not alter the effect of shear design parameters on Vc. This paper investigates the validity of such a practice. Data on 656 reinforced concrete beams were used to train an artificial neural network model to predict the shear capacity of reinforced concrete beams and evaluate the performance of several existing shear strength calculation procedures. A parametric study revealed that the effect of shear reinforcement on the shear strength of RC beams decreases at a higher reinforcement ratio. It was also observed that the concrete contribution to shear resistance, Vc, in RC beams with shear reinforcement is noticeably larger than that in beams without shear reinforcement, and therefore most current shear design procedures provide conservative predictions for the shear strength of RC beams with shear reinforcement

Evaluation of Shear Capacity of FRP-Reinforced Concrete Beams

Existing methods for calculation of shear capacity of concrete beams reinforced with fiber-reinforced polymer (FRP) are generally based on slightly modified versions of well established semi-empirical shear design equations. These equations were primarily derived from experimental data generated on concrete beams having steel reinforcement. However, FRP materials have different mechanical properties and consequently exhibit different modes of failure than steel, making the extension of existing shear design equations for steel-reinforced concrete (S-RC) beams to cover concrete beams reinforced with FRP somehow inaccurate. Current available methods include ACI 440-06, JSCE-97, CSA S806-02, and ISIS Canada-01. Availability of FRP reinforcement products varies in terms of capacity and modulus of elasticity, which can result in a significant change in behavior. An experimental database of 150 FRP-reinforced concrete (FRP-RC) beams was collected from published literature. Subsequently, this database was used to assess the validity of these four main existing shear design methods for FRP-RC beams. This study investigates the performance of the abovementioned design methods to estimate the nominal shear capacity, Vn of steel-free concrete beams reinforced with FRP bars. Results show that current design guidelines provide a shear strength underestimation in the case of beams without shear reinforcement and a shear strength overestimation for beams with shear reinforcement