Abstract
Stone Mastic Asphalt (SMA) is a tough, stable, rut-resistant mixture that takes advantage of the stone-to-stone contact to provide strength and durability for the material. Besides, the warm mix asphalt (WMA) technology allows reducing emissions and energy consumption by reducing the production temperature by 30–50 °C, compared to conventional hot mix asphalt technology (HMA). The dynamic modulus |E*| has been acknowledged as a vital material property in the mechanistic-empirical design and analysis and further reflects the strains and displacements of such layered pavement structures. The objective of this study is twofold, aiming at favoring the potential use of SMA with WMA technique. To this aim, first, laboratory tests were conducted to compare the performance of SMA and HMA through the dynamic modulus. Second, an advanced hybrid artificial intelligence technique to accurately predict the dynamic modulus of asphalt mixtures was developed. This hybrid model (ANN-TLBO) was based on an Artificial Neural Network (ANN) algorithm and Teaching Learning Based Optimization (TLBO) technique. A database containing the as-obtained experimental tests (96 data) was used for the development and assessment of the ANN-TLBO model. The experimental results showed that SMA mixtures exhibited higher values of the dynamic modulus |E*| than HMA, and the WMA technology increased the dynamic modulus values compared with the hot technology. Furthermore, the proposed hybrid algorithm could successfully predict the dynamic modulus with remarkable values of R2 of 0.989 and 0.985 for the training and testing datasets, respectively. Lastly, the effects of temperature and frequency on the dynamic modulus were evaluated and discussed.
Highlights
In recent decades, the manufacture and application of asphalt mixture have been significantly increasing because of excellent adhesion to mineral aggregates and viscoelastic properties [1,2]
For asphalt mixtures made by hot technology at a similar temperature, the difference was more pronounced with the higher frequency
The proposed model consisted of one well-known machine learning algorithm (ANN) and an effective optimization algorithm (TLBO)
Summary
The manufacture and application of asphalt mixture have been significantly increasing because of excellent adhesion to mineral aggregates and viscoelastic properties [1,2]. In order to address economic and environmental objectives, a number of asphalt mixtures were proposed, including dense-graded asphalt concrete, stone mastic asphalt (SMA), rubberized asphalt, crumb rubber asphalt concrete, and asphalt concrete incorporated [3,4,5]. From a practical viewpoint, mixing techniques were considered as an important stage for an asphalt mixture, gaining its targeted performances [3,4,6,7]. Dense-graded asphalt and stone mastic asphalt (SMA). Sci. 2020, 10, 5242 mixtures applying the hot (HMA) and warm (WMA) mixing techniques are two typical applications of asphalt mixtures that received more attention from researchers [4,8,9,10]
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