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Artificial neural networks (ANNs) and Gb*-based regression models were used for the prediction of the dynamic modulus (|E*|) of South Carolina's hot mix asphalt mixtures (HMAs) the majority of which contained recycled asphalt pavement (RAP). Models' training and testing were done using a database that contained 1656 |E*| values from 93 HMA mixtures. Gb*-based models included the Hirsch, revised Hirsch, Bari-Witczak, revised Bari-Witczak, Al-Khateeb 1, Al-Khateeb 2, NCHRP 1-40D, and the simplified global models. The results showed that Gb*-based regression models had a significant bias in prediction; Coupling VMA and Gb* had the most influence on |E*|; four-layer ANNs generally had a better performance than three-layer ANNs on using Hirsch model's related inputs; ANN 3-15-15-1 and ANN 8-15-15-1 (developed with similar input variables as the Hirsch and Witczak regression models respectively) showed very high performance of R2 > 0.994 on testing. Therefore, ANNs could be considered to capture the influence of the binders' rheological properties, mixture's volumetric properties, and RAP on |E*| of HMA mixtures far better than regression-based models. [ABSTRACT FROM AUTHOR]

In recent years, nanotechnology has been utilized in many engineering applications. In this technology, nano-sized particles are used in improving the properties of various materials. The construction industry, in general, has not embraced this technology; however, these particles could be used to enhance the physical and chemical properties of the materials such as asphalt binders. The objective of this study was to investigate and evaluate the rheological properties of three asphalt binder sources containing various percentages of carbon nano particles. The experimental design for this study included the utilizations of three binder sources (PG 64-22), one type of nano particle, and four nano dosage percentages (0.2%, 0.5%, 1.0%, and 1.5% by weight of the binder). The following rheological properties were tested and evaluated: viscosity, performance grade, creep and creep recovery, and frequency sweep. The results of the experiments indicated that the addition of nano particles was helpful in increasing the viscosity, failure temperature, complex modulus, and elastic modulus values as well as in improving rutting resistance of the binder. On the other hand, the content analysis of nano particles showed that a relatively low percentage (<0.2%) of nano particles did not have a significant effect on improving the binder properties while a relative high percentage (>1.0%) of nano particles is recommended for the modification of an asphalt binder [ABSTRACT FROM AUTHOR]

In recent years, nano technology, a relatively new field in science dealing with structures that are on the nano scale, have been used in numerous applications to improve properties of various materials. However, the use of this technology has not been explored in detail in the area of asphalt binder materials. The objective of this study was to investigate and evaluate the high temperature rheological properties of the binders containing various percentages of carbon nano particles. The experimental design for this study included the utilization of five binder sources, three binder grades (PG 64-22, PG 64-16, and PG 52-28), one type of nano particle, and four percentages of nano particles (0.0 %, 0.5 %, 1.0 %, and 1.5 % by weight of the virgin binder). Some of the rheological characteristics of these binders were obtained, including rotational viscometer (four testing temperatures of 120, 135, 150 ,and 165°C) and dynamic shear rheometer. The results of the experiments indicated that the addition of nano particles was helpful in increasing the viscosity, failure temperature, phase angle, and viscous and elastic modulus values and in improving the rutting resistance of the binders. On the other hand, the binder sources and grades generally influenced their rheological properties as the nano particles were added to the asphalt binder. [ABSTRACT FROM AUTHOR]