Predicting shear strength of fiber-reinforced concrete beams reinforced with longitudinal FRP bars with machine learning techniques

This study investigates the shear strength prediction of Fibre-Reinforced Concrete (FRC) beams reinforced with Fibre-Reinforced Polymer (FRP) bars and without stirrups through the application of Machine Learning (ML) techniques. The utilisation of FRP bars, particularly Glass Fibre-Reinforced Polymer (GFRP) and Basalt Fibre-Reinforced Polymer (BFRP) bars, has emerged as a promising alternative to traditional steel reinforcement due to their superior mechanical properties and corrosion resistance. Moreover, the incorporation of macro- and micro-discrete fibres into concrete compositions enhances both shear and flexural behaviour while reducing crack propagation induced by tensile stresses. The primary objective of this research is to develop accurate predictive models for the shear capacity of FRP-reinforced concrete beams, considering various influential parameters. Six different ML techniques, namely Multiple Linear Regression (MLR), Gaussian Process Regression (GPR), k-Nearest Neighbors (KNN), Support Vector Machines (SVMs), Artificial Neural Networks (ANNs) and Random Forest Regression (RFR), are employed to analyse the complex interactions between input variables, such as fibre type, bar diameter, concrete compressive strength, beam dimensions and the resulting shear strength. By leveraging these computational approaches, we aim to overcome the limitations of conventional analytical methods and provide robust predictions for structural design and optimisation.