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

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

Warm Mix Asphalt Additive in Natural Rubber Modified Bitumen

“…Overall, there were significant differences in viscosity between modified binder containing additive and one without additive. Rheology performance is very important in order to study the influence of mixing process and condition to the non solid material characteristic [11]. The effect of warm mix additive on Natural Rubber Modified Bitumen on the complex modulus (G*) which is related to the viscoelasticity property is illustrated in Figure 4.…”

Performance Evaluation of Warm Mix Asphalt Mixtures with Recycled Asphalt Pavement

“…In addition, the temperature susceptibility of the bitumen samples has been calculated in terms of penetration index (PI) using the results obtained from penetration and softening point tests [35] The viscosity is defined as resistance of a fluid to flow and it affects the workability of the bitumen [36]. Brookfield viscometer was employed to inspect the mixing and compaction temperatures of the mixtures in according to ASTM D4402-06 [37].…”

Neural network modeling of SBS modified bitumen produced with different methods

“…The statistical analysis and artificial neural networks were used to create mathematical models for the prediction of the bitumen viscosity. The comparison between experimental data and simulated results with the generated models exhibited best performance of the neural networks analysis in contrast to the statistic models [15]. It was reported that the ANN model gives satisfactory results for estimating the deflection of pavement according to layer thickness [16].…”