Using Hybrid Artificial Intelligence Approaches to Predict the Fracture Energy of Concrete Beams

Abstract

Fracture energy is always used to represent the fracture performance of concrete structures/beams, which is crucial for the application of concrete. However, due to the nature of concrete material and the complexity of the fracture process, it is difficult to accurately determine the fracture energy of concrete and predict the fracture behavior of different concrete structures. In this study, artificial intelligence approaches were tried to seek a feasible way to solve these prediction issues. Firstly, the ridge regression (RR), the classification and regression tree (CART), and the gradient boosting regression tree (GBRT) were selected to construct the predictive models. Then, the hyperparameters were tuned with the particle swarm optimization (PSO) algorithm; the performances of these three optimum models were compared with the test dataset. The mean squared errors (MSEs) of the optimum RR, CART, and GBRT models were 0.0447, 0.0164, and 0.0111, respectively, which indicated that their performances were excellent. Compared with the RR and CART models, the hybrid model constructed with GBRT and PSO appeared to be the most accurate and generalizable, both of which are significant for prediction work. The relative importance of the variables that influence the fracture energy of concrete was obtained, and compressive strength was found to be the most significant variable.