Improvement of a hybrid evolutionary model of genetic algorithms and artificial neural networks

被引：0

作者：

Will, Adrián Luis Ernesto ^{[1
,2
]}

机构：

[1] Facultad de Ciencias Exactas y Tecnología, Universidad Nacional de Tucumán, Facultad Regional de Tucumán, Universidad Tecnológica Nacional, Tucumán, Argentina

[2] Grupo de Investigación de Tecnologías Avanzadas de Tucumán, Argentina

来源：

Boletin Tecnico/Technical Bulletin | 2016年 / 54卷 / 03期

关键词：

Compressive strength - Genetic algorithms - Fiber reinforced materials - Concrete mixtures - Mixtures - Reinforced concrete;

D O I：

暂无

中图分类号：

TU528 [混凝土及混凝土制品]; TV43 [水工混凝土和砂浆];

学科分类号：

摘要：

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.

引用

页码：107 / 116