Resumen

GONZALEZ SALCEDO, Luis Octavio; GUERRERO ZUNIGA, Aydee Patricia; DELVASTO ARJONA, Silvio  y  ERNESTO WILL, Adrián Luis. Development of a Hybrid Evolutionary Model of Genetic Algorithms and Artificial Neural Networks for Metal Fiber and Reinforced Concrete Mixture Dosage. rev.ing. [online]. 2015, n.43, pp.46-54. ISSN 0121-4993.  https://doi.org/10.16924/riua.v0i43.874.

An evolutionary model is developed in a computing environment to propose metal fiber reinforced concrete mixture dosages for compressive strength applications. The model is hybrid as it includes both a dosage system based on genetic algorithm and a properties prediction system based on artificial neural networks. The results obtained are compared with experimentally reported dosages set, and the comparisons show an approximation in the simulation process. Given the characteristics of the model, it is considered a contribution to concrete technology.

Palabras clave : Genetic Algorithms; Concrete Mixture Dosages; Evolutionary Model; Artificial Neural Networks.

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