Computational parameter identification of strongest influence on the shear resistance of reinforced concrete beams by fiber reinforcement polymer

Bars made of fiber reinforcement polymer (FRP) are in common usage for concrete reinforcing instead of steel reinforcing since steel could be affected by corrosion. The concrete beams reinforced by FRP bars have been studied mostly in longitudinal direction without shear reinforcement. The primary objective of this investigation was to design and advance an algorithm for selection procedure of the parameters influence on prediction of shear resistance of reinforced concrete beams by FRP. Six input parameters were used which represent geometric and mechanical properties of the bars as well as shear features. These parameters are: web width, tensile reinforcement depth, ratio of shear and depth, concrete compressive strength, ratio of FRP reinforcement, FRP modulus of elasticity and beam shear resistance. The searching algorithm is based on combination of artificial neural network and fuzzy logic principle or adaptive neuro fuzzy inference system (ANFIS). Based on the obtained results ratio of shear and depth has the strongest influence on the prediction of shear resistance of reinforced concrete beams by FRP. Moreover, combination of tensile reinforcement depth and ratio of shear and depth is the most influential combination of two parameters on the prediction of shear resistance of reinforced concrete beams by FRP. Finally, combination of tensile reinforcement depth, ratio of shear and depth and FRP modulus of elasticity is the most influential combination of three parameters on the prediction of shear resistance of reinforced concrete beams by FRP.