Fiber-reinforced polymer (FRP) materials are increasingly used in the construction and transportation industries, generating growing volumes of waste. This study applied a machine learning model to predict the compressive strength of eco-friendly concrete incorporating recycled glass-fiber-reinforced polymer (GFRP) waste. Based on 119 laboratory mixes, the model achieved a good prediction accuracy (R² = 0.8284 on the test set). The analysis indicated that compressive strength tends to decrease at higher GFRP dosages, with relatively favorable performance observed at low contents. The two most influential factors were the water-to-cement ratio and the total GFRP content. The physical form of the recycled material was also important: powders and fibers generally showed positive effects, while coarse aggregate replacement was less effective. This machine learning-based approach offers preliminary quantitative guidance on mix design with GFRP waste and highlights opportunities for reusing industrial by-products in more sustainable concretes.

Concrete beams are commonly used in construction projects to provide structural support. Reinforcing these beams with steel and Glass Fiber Reinforced Polymer (GFRP) can increase their strength and durability. Steel reinforcement is a traditional method used for decades, while GFRP is a newer material that offers several advantages, including corrosion strength and lighter weight. Combining both materials to reinforce concrete beams can result in a stronger and more resilient structure, making it an ideal choice for many construction projects. In this study, the behavior of reinforced concrete beams with steel and GFRP reinforcement is numerically investigated, and the effect of steel percentage and GFRP percentage on the mechanical strength and energy response of the beam is determined using the Response Surface Methodology (RSM). Using the ABAQUS software, a widely used Finite-Element Analysis (FEA), the numerical modeling is first verified, and then targeted analyses are performed on the beam using the tests specified by the RSM. Then, based on the strength and energy of the beam, a two-variable Analysis of Variance (ANOVA) and two-objective optimization are performed to investigate the effect of changing each parameter of steel percentage and GFRP percentage on beam strength and energy. Several laboratory studies have been conducted on the behavior of concrete beams with steel and GFRP bars, but no research has statistically and numerically examined their behavior. We also simultaneously optimized both strength and energy parameters based on the ratio of steel and GFRP and compared them with numerical values. We show an interaction relationship between steel and GFRP ratio in the strength and energy of concrete beams. In the beam with hybrid reinforcement, with the increase of steel and GFRP ratios, the amount of strength and energy does not increase linearly, and there is a quadratic relationship between them.