Resumen
In project practice, the search for optimal solutions is based on the traditional process of trial and error, which consumes much time and does not guarantee that solutions found are the optimal solutions for the problem. Many studies have been developed in recent years with the aim of solving problems in various fields of structural engineering with the aid of intelligent algorithms; however, when it comes to the optimization of structural designs, the approaches considered by the authors involve a large number of variables and constraints, making the implementation of optimization techniques difficult and consuming significant processing time. This research aims to evaluate the efficiency of intelligent algorithms when associated with structural optimization approaches that are simpler to implement. Therefore, a Genetic Algorithm in Real Coding was built to serve as an auxiliary tool for pre-dimensioning prestressed concrete beams. With this, the problem becomes simpler to implement, as it depends on a smaller number of variables, leading to less processing time consumption. Simulations were performed to calibrate the Genetic Algorithm and find the optimal solution later. The solution found by the algorithm was compared with the real solution of a project that had already gone through a traditional optimization process. Even in these circumstances, the proposed Genetic Algorithm was able to find, in 210 s, a more economical solution. Our studies found that even with more straightforward approaches, intelligent algorithms can help in the search for optimal solutions to structural engineering problems; in addition, using real coding in fact proved to be a great strategy due to the nature of the problem, making the implementation of the algorithm simpler and ensuring answers with little processing time.