Punching shear of concrete flat slabs reinforced with fibre reinforced polymer bars

Abstract

Fibre reinforcement polymers (FRP) are non-corrodible materials used instead of conventional steel and have been approved to be an effective way to overcome corrosion problems. FRP, in most cases, can have a higher tensile strength, but a lower tensile modulus of elasticity compared to that of conventional steel bars. This study aimed to examine flat slab specimens reinforced with glass fibre reinforced polymer (GFRP) and steel bar materials for punching shear behaviour. Six full-scale two-way slab specimens were constructed and tested under concentric load up to failure. One of the main objectives is to study the effect of reinforcement spacing with the same reinforcement ratio on the punching shear strength. In addition, two other parameters were considered, namely, slab depth, and compressive strength of concrete. The punching shear provisions of two code of practises CSA S806 (Canadian Standards 2012) and JSCE (JSCE et al. 1997) reasonably predicted the load capacity of GFRP reinforced concrete flat slab, whereas, ACI 440 (ACI Committee 440 2015) showed very conservative load capacity prediction. On the other hand, a dynamic explicit solver in nonlinear finite element (FE) modelling is used to analyse a connection of column to concrete flat slabs reinforced with GFRP bars in terms of ultimate punching load. All FE modelling was performed in 3D with the appropriate adoption of element size and mesh. The numerical and experimental results were compared in order to evaluate the developed FE, aiming to predict the behaviour of punching shear in the concrete flat slab. In addition, a parametric study was created to explore the behaviour of GFRP reinforced concrete flat slab with three parameters, namely, concrete strength, shear load perimeter to effective depth ratio, and, flexural reinforcement ratio. It was concluded that the developed models could accurately capture the behaviour of GFRP reinforced concrete flat slabs subjected to a concentrated load. Artificial Neural Networks (ANN) is used in this research to predict punching shear strength, and the results were shown to match more closely with the experimental results. A parametric study was performed to investigate the effects of five parameters on punching shear capacity of GFRP reinforced concrete flat slab. The parametric investigation revealed that the effective depth has the most substantial impact on the load carrying capacity of the punching shear followed by reinforcement ratio, column perimeter, the compressive strength of the concrete, and, the elastic modulus of the reinforcement.