Abstract
Prestressing in steel beams offers significant benefits in terms of structural efficiency and material savings. The aim of this study is to present a comparative analysis of the design results of prestressed steel beams obtained through three optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and its variant, including a craziness operator for performance enhancement (CRPSO). These algorithms were used to minimize CO2 emissions and cost in the structural design process, while adhering to the constraints imposed by ABNT NBR 8800:2008. The implementation was carried out using MATLAB (2016). The validation of the methodology was conducted using examples from the literature, comparing the CO2 emissions and cost values obtained from experimental work and those derived from GA, PSO, and CRPSO. The results indicate that, although the Genetic Algorithm is widely used in optimization, more optimized results are achieved using PSO. Furthermore, the improvement obtained by CRPSO showed no significant deviation from those obtained by PSO, nor did it affect the convergence speeds of the results. It was also observed that all three implemented models yielded more optimized values of emissions and costs compared to those reported in the literature.
Published Version
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