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