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Modeling of asphalt-rubber rotational viscosity by statistical analysis and neural networks

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Modeling of asphalt-rubber rotational viscosity by statistical analysis and neural networks

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Título Modeling of asphalt-rubber rotational viscosity by statistical analysis and neural networks
Autor Specht, Luciano Pivoto
Khatchatourian, Oleg A.
Brito, Lélio Antonio Teixeira
Ceratti, Jorge Augusto Pereira
Abstract 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.
Contido em Materials research : ibero-american journal of materials. São Carlos, SP. vol. 10, no. 1 (Jan./Mar. 2007), p. 69-74
Assunto Pavimentação : Borracha
Redes neurais artificiais
[en] Artificial neural network
[en] Asphalt-rubber
[en] Modeling
[en] Viscosity
Origem Nacional
Tipo Artigo de periódico
URI http://hdl.handle.net/10183/20542
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