Combining Neural Network Models to Prediction the Bond Strength of Glass FRP to Concrete

The use of FRP and other composite materials as bar or sheets is one of the most technically and economically viable options in the construction, refurbishment, and reinforcement of structures such as concrete structures. One of the most important issues to consider when using such materials is their bond strength to structural concrete. In this paper, the effect of combining ensemble prediction models with single estimation models on improving the results of single models is estimated to estimate the bond strength of GFRP bars to concrete. To this end, neural networks with predictive results inputs are first used to estimate the bond strength of GFRP to improve the best model result from the two previous models- Be. Then, by considering the prediction outputs of the first neural network model and the best single model above mentioned as input, the neural networks are again used to present a better model than the first ANN model. The final results show the reduction of the prediction error of the ANN model combined with single and ensemble methods compared to the single models previously presented, the weighted average output model of the two single models above and the ANN model. The combination of the two models usefulness a single.