• Journal of the Korea institute for structural maintenance and inspection
• /
• v.17 no.6
• /
• pp.112-121
• /
• 2013

FRP reinforced lightweight concrete structures can offer corrosion resistance and weight reduction effect simultaneously, so practical use of the structures may be expected afterwards. But to make concrete structures using lightweight concrete and FRP bar, that can resist external forces without internal slip of the FRP bar, it is very important to understand bond characteristic between lightweight concrete and FRP bar. During that time, a lot of studies for bond behaviors of FRP bar in normal concrete were conducted, but studies for bond behavior of FRP bar in lightweight concrete are very limited to date. So, bond characteristic between lightweight concrete and helically deformed GFRP bar was investigated in this study. Three main parameters were considered in experimental investigation: type of rebar, concrete type, and compressive strength of lightweight concrete. As an experimental result, it could be known that bond strength of helically deformed GFRP bar in lightweight concrete was 0.49 times bond strength of steel reinforcement in normal concrete.

• Computers and Concrete
• /
• v.23 no.1
• /
• pp.49-60
• /
• 2019

In recent years, multiple experimental studies have been performed on using fiber reinforced polymer (FRP) bars in reinforced concrete (RC) structural members. FRP bars provide a new type of reinforcement that avoids the corrosion of traditional steel reinforcement. In this study, predicting the shear strength of RC beams with FRP longitudinal bars using artificial neural networks (ANNs) is investigated as a different approach from the current specific codes. An ANN model was developed using the experimental data of 104 FRP-RC specimens from an existing database in the literature. Seven different input parameters affecting the shear strength of FRP bar reinforced RC beams were selected to create the ANN structure. The most convenient ANN algorithm was determined as traingdx. The results from current codes (ACI440.1R-15 and JSCE) and existing literature in predicting the shear strength of FRP-RC beams were investigated using the identical test data. The study shows that the ANN model produces acceptable predictions for the ultimate shear strength of FRP-RC beams (maximum $R^2{\approx}0.97$). Additionally, the ANN model provides more accurate predictions for the shear capacity than the other computed methods in the ACI440.1R-15, JSCE codes and existing literature for considering different performance parameters.