Estimating Model Parameters of Concrete Materials Based on Neural Networks

Abstract

Based on the observed deformation data of concrete specimens in a laboratory, the parameter identification procedure is proposed to identify model parameters of concrete materials in the discrete element model. The sigmoid function is used as an activation function to transform the input of neural node into node output. The weights and bias of neural network are adjusted by using the optimization algorithm during training neural network. The macro-experiment for vertical compression specimens of concrete material is performed in a laboratory. Based on macro-experimental data and neural network model, model parameters of concrete materials in the discrete element model are determined. The investigation shows that simulated stress–strain curve approaches the experimental curve very well. The effectiveness of the proposed determining procedure for model parameters of concrete materials in a discrete element model is validated.