Neural network model for bond strength of FRP bars in concrete

Abstract

Interest in FRP composite bars as reinforcement to concrete has increased over the years as it showed solutions to the drawbacks of steel such as its corrosion issues and vulnerability when employed in adverse environmental conditions. However, it is still not widely incorporated as a replacement to conventional steel primarily due to the complexity of its bond strength mechanism. This, therefore, imposes the need to establish a comprehensive relationship for the bond property of the FRP reinforced concrete. This paper developed a novel Artificial Neural Network (ANN) bond strength prediction model for FRP reinforced concrete using 184 hinged beam database from various existing experiments. From series of simulations performed, the model N 7-10-1 with ten nodes in the hidden layer appeared to be the best fit with the experimental results yielded the most favorable performance among other existing models. From the parametric analysis conducted, the compressive strength of the FRP reinforced concrete has proved to be the most dominant parameter in evaluating its bond behavior as determined by relative importance of 17.82%. Overall, the proposed ANN model has demonstrated the best prediction for FRP bond strength in comparison to previous studies and code equations.