Intelligent Proportion Optimization of SFRC Using Ensemble Learning: A Multi-Objective Predictive Framework

Abstract

Steel fiber-reinforced concrete (SFRC) is formulated by incorporating short, slender steel fibers into standard concrete, thereby creating a composite material. This addition significantly mitigates the brittle nature of concrete, enhancing its strength, toughness, and durability. The application of SFRC not only improves structural safety and service life but also promotes the development of green building materials and efficient construction technologies. To optimize the mix proportion design of SFRC, key parameters such as steel fiber content, water, cement, sand, natural aggregates, and water reducer were collected. A neural network model was constructed to leverage its powerful nonlinear mapping capabilities, establishing an implicit relationship between the mix proportions and compressive strength. The trained model enables rapid prediction of SFRC compressive strength, while a genetic algorithm was employed to inversely search for the optimal mix proportions that meet target performance requirements, providing a novel approach and design strategy for the intelligent design of SFRC.