Reducing the environmental impact of prestressed composite beams via SABO algorithm

The global expansion of the construction industry, the use of steel and concrete, materials whose production is one of the primary sources of greenhouse gas emissions, has increased significantly. In the search for more sustainable solutions, applying prestressing in composite steel and concrete elements emerges as a promising alternative to reduce material consumption. To contribute to the research on prestressed composite structures, this study presents an optimal design formulation for welded composite beams with external prestressing, based on Brazilian normative codes, aiming to minimize CO₂ emissions in manufacturing. In addition to the dimensions of the steel beam profiles, the design variables included the slab's steel decking, the concrete's compressive strength, and the number of tendons. The solution to the optimization problem was obtained using the Self-Adaptive Bonobo Algorithm and the Particle Swarm Optimization Algorithm. The validation and suitability of the formulation were confirmed by comparing its results with experimental and numerical data. A parametric analysis with spans ranging from 5 to 40 m was conducted to identify which geometric parameters most influence total CO₂ emissions. Based on the results, it can be concluded that prestressing is necessary for beams with spans greater than 25 m. Final CO₂ emissions are also highly influenced by the metal formwork and beam profile. Although higher-strength concretes emit more CO₂, the best solutions for concretes above 25 MPa were found. Finally, between the two algorithms analyzed, the Self-Adaptive Bonobo Algorithm provided the best solutions.