A Smart GUI for Predicting Load Capacity and Ultimate Deflection of Steel Fibre Reinforced Concrete Beams Using Machine Learning

This study presents a user-friendly Graphical User Interface (GUI) designed to predict the load capacity and mid-span deflection at failure of steel fibre reinforced concrete (SFRC) beams, offering an innovative material solution for determining the structural performance of concrete elements. The GUI employs advanced machine learning techniques validated against a comprehensive dataset of 88 experimental records of SFRC beams with hooked-end steel fibres gathered from the literature. The tool demonstrated high predictive accuracy and robustness, with results closely matching experimental outcomes, making it a valuable and practical resource for researchers and engineers. By enabling efficient estimation of beam performance under four-point bending conditions, the GUI significantly reduces the time and cost associated with traditional experimental or numerical methods. While the detailed computational framework will be elaborated in future publications, this paper focuses on the development, validation, and practical applications of the tool, highlighting its potential to enhance decision-making and optimize the design process in the field of SFRC beam analysis.