Predicting Bond Strength between Externally Bonded FRP and Heat-damaged Concrete Using ANN

Over the past 20 years, numerous experimental and numerical studies have been conducted to understand the bond characteristics of externally bonded (EB) FRP-intact (heat-damaged) concrete joints. Consequently, a large database became available to develop models for bond-strength prediction at these joints. An artificial neural network (ANN)-bond model was built, trained and tested using MATLAB?, utilizing more than 500 data points before being statistically analyzed to substantiate its validity for field predictability. The ANN model converged fully at 405 epochs with natural distribution of training and testing data noticed. The use of fourteen hidden layers provided the least prediction error. The performance of the first bond model to consider the impact of elevated temperature was compared to that of well-known literature models The present model demonstrated higher prediction superiority over the different literature models as indicated by the present detailed statistical and sensitivity analyses. For example, using a group of prediction data, the coefficient of determination (R2) and the root mean square error for the present model attained their highest and lowest values at 0.88 and 0.15, respectively, compared to those of the other models tested. The present model captures the trend behavior of bond strength versus the key parameters; namely, compressive strength, maximum aggregate size of concrete, FRP thickness, elastic modulus, as well as FRP bond length and width ratio. The model reflected higher sensitivity to exposing concrete to elevated temperatures than that by the different literature models. The impact of the key ?