Influence of genetic algorithm parameters on the structural optimization of open web steel beams

Resumo

Structural optimization has emerged as an essential tool in engineering, driven by the growing demand for more conscious and efficient use of resources. This context supports the adoption of computational optimization techniques in civil construction projects, especially in the development of lighter structural elements with improved mechanical performance. Silva and Lübke[1] applied the finite element software FEMOOP, developed in C++, for the modeling and analysis of steel open web beams. The software includes the formulation of a three-node triangular finite element under plane stress conditions, along with specific routines for shape optimization and the detailing of the ideal cutting line based on the obtained model. The present study aims to evaluate the influence of varying parameters used in genetic algorithms on the results produced by the implemented optimization routine. Different parameter combinations—such as initial population size, crossover rate, mutation rate, and elitism—were tested and applied to the optimization of different open web beams. The results indicate that proper calibration of these parameters can significantly affect the algorithm’s performance and the quality of the solutions generated, directly impacting the load-bearing capacity of the optimized beams. Thus, this study contributes to the advancement of the structural optimization process, providing guidance for selecting more efficient configurations when applying genetic algorithms to the design of web open steel beams.