Prediction of Adhesion Parameters of Hook-shaped Steel Fibers and Concrete Using Artificial Neural Networks

As steel fibers are important reinforcement materials in concrete, in this study, the behavior of hook-shaped steel fibers from concrete is predicted through the use of artificial neural networks. In the absence of comprehensive laboratory data, data obtained from finite element analysis was used for modeling. The simulations are carried out using ABAQUS software's finite element method in 3D. Using the concept of the transition zone of the interface, whose parameters were obtained by inverse finite element analysis and experimental tests conducted on a sample of fibers, this model has been developed to simulate the interaction between fibers and concrete. On the basis of the results of the numerical model validated against the experimental results, the effective parameters of the fibers were extracted, and a neural network was then constructed based on the results. A multilayer forward perceptron artificial neural network and back-propagation training algorithm are used to predict pull-out force, with Marquardt-Lonberg optimization applied. The results demonstrate that the neural network model presented in this research is an effective method for predicting the pull-out force of fibers from concrete, in part because it allows the use of more variables in modeling, as well as delivering more accurate results.