Modeling of asphalt-rubber rotational viscosity by statistical analysis and neural networks
Título del documento: Modeling of asphalt-rubber rotational viscosity by statistical analysis and neural networks
Revue: Materials research
Base de datos: PERIÓDICA
Número de sistema: 000312965
ISSN: 1516-1439
Autores: 1

2
Instituciones: 1Universidade Regional do Noroeste do Estado do Rio Grande do Sul, Ijui, Rio Grande do Sul. Brasil
2Universidade Federal do Rio Grande do Sul, Escola de Engenharia, Porto Alegre, Rio Grande do Sul. Brasil
Año:
Periodo: Ene-Mar
Volumen: 10
Número: 1
Paginación: 69-74
País: Brasil
Idioma: Inglés
Tipo de documento: Nota breve o noticia
Enfoque: Experimental
Resumen en inglés It is of a great importance to know binders' viscosity in order to perform handling, mixing, application processes and asphalt mixes compaction in highway surfacing. This paper presents the results of viscosity measurement in asphalt-rubber binders prepared in laboratory. The binders were prepared varying the rubber content, rubber particle size, duration and temperature of mixture, all following a statistical design plan. The statistical analysis and artificial neural networks were used to create mathematical models for prediction of the binders viscosity. The comparison between experimental data and simulated results with the generated models showed best performance of the neural networks analysis in contrast to the statistic models. The results indicated that the rubber content and duration of mixture have major influence on the observed viscosity for the considered interval of parameters variation
Disciplinas: Ingeniería,
Física y astronomía
Palabras clave: Ingeniería civil,
Ingeniería de materiales,
Mecánica, elasticidad y reología,
Carreteras,
Viscosidad,
Asfalto,
Caucho,
Propiedades mecánicas
Keyword: Engineering,
Physics and astronomy,
Civil engineering,
Materials engineering,
Mechanics, elasticity and rheology,
Highways,
Viscosity,
Asphalt,
Rubber,
Mechanical properties
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