Identification of the interfacial cohesive law parameters of FRP strips externally bonded to concrete using machine learning techniques

Abstract

A machine learning-based artificial neural network (ANN) approach is developed to automatically identify the interfacial cohesive parameters between fiber-reinforced polymers (FRPs) and concrete. A refined finite element (FE) model employing a cohesive zone model is established to simulate the interfacial Mode-II fracture. According to the database of load–displacement responses generated from the FE model, the trained ANN model can accurately and concurrently identify the cohesive law parameters. Moreover, based on a finite set of training data, the proposed approach shows high accuracy for the cases whose interfacial properties fall within the gap in or outside of the training dataset.