Predicting behavior of FRP-confined concrete using neuro fuzzy, neural network, multivariate adaptive regression splines and M5 model tree techniques

This paper studies the ability of artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS), multivariate adaptive regression splines (MARS) and M5 Model Tree (M5Tree) techniques to predict ultimate conditions of fiber-reinforced polymer (FRP)-confined concrete. A large experimental test database that consists of over 1000 axial compression tests results of FRP-confined concrete specimens assembled from the published literature was used to train, test, and validate the models. The modeling results show that the ANN, ANFIS, MARS and M5Tree models fit well with the experimental test data. The M5Tree model performs better than the remaining models in predicting the hoop strain reduction factor and strength enhancement ratio, whereas the ANN model provided the most accurate estimates of the strain enhancement ratio. Performances of the proposed models are also compared with those of the existing conventional and evolutionary algorithm models, which indicate that the proposed ANN, ANFIS, MARS and M5Tree models exhibit improved accuracy over the existing models. The predictions of each proposed model are subsequently used to establish the interdependence of critical parameters and their influence on the behavior of FRP-confined concrete, which are discussed in the paper.