Otimização De Pavimentos De Concreto Protendido via Algoritmos Genéticos

Silva, Michell Ribeiro Lopes e; Sobrinho, Francisco Rosendo; Melo, Álisson José Maia; Parente, Evandro; Mota, João Pedro Alexandre Silva

doi:10.20906/cps/cilamce2017-0923

Cited by 1 publication

(2 citation statements)

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“…It is evident, analyzing the results of [8][9][10][11][12][13], that the metaheuristic techniques, particularly the GA, have presented themselves as a good alternative for optimizing prestressed elements, corroborating with the reports of [4][5][6][7]. However, as shown in [13], the way the problem is approached can significantly influence the processing time of the optimization algorithm.…”

Section: Introductionmentioning

confidence: 55%

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Use of Real Coded Genetic Algorithm as a Pre-Dimensioning Tool for Prestressed Concrete Beams

et al. 2023

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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.

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Section: Introductionmentioning

confidence: 55%

“…However, both methods did not provide satisfactory results due to the nature of the problem, which led the authors to work with a combination of Multi-Objective Evolutionary Algorithms with GA to find the optimal solution. The authors of [10] used a traditional Genetic Algorithm to optimize prestressed unidirectional ribbed slabs.…”

Section: Introductionmentioning

confidence: 99%