ANN-Based Model for the Prediction of the Bond Strength between FRP and Concrete

In the last decades, the uses of fiber reinforced polymer (FRP) composites in the structural strengthening of reinforced concrete (RC) structures have become the state of the art, providing a valid alternative to the traditional use of steel plates. These relatively new materials present, in fact, great advantages, including high corrosion resistance in aggressive environments, low specific weight, high strength-to-mass-density ratio, magnetic and electric neutrality, low axial coefficient of thermal expansion and sustainable costs of installation. In flexural and shear strengthening of RC members, the effectiveness of the epoxy bonded FRP strongly depends on the adhesion forces exchanged with the concrete substrate. When the flexural moment is present, the FRP strengthening is activated through the stress transfer on the tension side, which is guaranteed by the contact beam region to which the adhesive is bonded to the beam itself. Hence, the determination of the maximum forces that cause debonding of the FRP-plate becomes crucial for a proper design. Over the years, many different analytical models have been provided in the scientific literature. Most of them are based on the calibration of the narrow experimental database. Now, hundreds of experimental results are available. The main goal of the current study is to present and discuss an alternative theoretical formulation for predicting the debonding force in an FRP-plate, epoxy-bonded to the concrete substrate by using an artificial neural networks (ANNs) approach. For this purpose, an extensive study of the state of the art, reporting the results of single lap shear tests, is also reported and discussed. The robustness of the proposed analytical model was validated by performing a parametric analysis and a comparison with other existing models and international design codes, as shown herein.