Shear capacity of slender FRP-RC beams utilizing a hybrid ANN with the firefly optimizer

Abstract

The popularity of fiber-reinforced polymer (FRP) bars as a structural element has soared due to their advantageous mechanical and physical properties. Despite an abundance of code requirements and heuristic equations, engineers specializing in structural retrofitting and analysis often struggle to utilize a suitable yet precise equation. This study introduces a novel approach by presenting a firefly optimization algorithm (FOA) combined with an artificial neural network (ANN)—termed as FOA-ANN—as an advanced hybrid machine learning model. The primary objective is to predict the shear capacity of slender FRP reinforced concrete (FRP-RC) beams without stirrup. An extensive experimental database of slender FRP-RC beams without stirrup was compiled. Leveraging this database and the proposed hybrid method, a simple yet accurate closed-form equation for determining the shear capacity of slender FRP-RC beams without stirrup was formulated. Additionally, a selection of pre-existing equations was provided for comparison of accuracy. Results indicate that the suggested FOA-ANN equation offers a more accurate alternative, outperforming equations derived from CSA S806-12 and AASHTO LRFD. The FOA-ANN hybrid technique proves to be highly effective in predicting the shear capacity of slender FRP-RC beams without stirrup.