Predicting the Shear Capacity of FRP in Shear Strengthened RC Beams using ANN and NID

This study aims at the utilization of machine learning techniques in investigating the effect of measured geometric and mechanical properties of shear-strengthened reinforced concrete (RC) beams on the shear capacity of fiber reinforced polymers (FRP). Two complementary machine learning techniques were used; artificial neural network (ANN) and neural interpretation diagram (NID). The input parameters obtained from an experimental database were used to construct an ANN model that was programmatically validated. The validated ANN model was used to generate a NID that visually identifies the input parameters which have a direct association with the shear capacity of FRP. Moreover, two ANN models were developed, the first model consisted of all the independent parameters and the second model contained only the selected independent parameters. As a result, the ANN model with the selected independent parameters yielded predictions that are in close agreement with the experimental results compared to the ANN model with all the independent parameters. Thus, the implementation of machine learning techniques has proven to be an adaptive tool that can be fully expanded to other areas in structural engineering.