# Artificial Intelligence-Based Model for the Prediction of Dynamic Modulus of Stone Mastic Asphalt
## 1 Introduction  
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]. 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]. Additionally, from a practical viewpoint, mixing techniques were also considered as an important stage for an asphalt mixture, gaining its targeted performances [3; 6; 7]. Among these, dense-graded asphalt and stone mastic asphalt (SMA)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]. Historically, hot mix asphalt (HMA) has been the most popular paving material for roadways [11; 12; 13; 14; 15]. The mechanism of this technology is based on high temperatures during mixing and compaction stages to ensure the workability and achieve the desired in-place density of the asphalt mixture. However, HMA mixtures disclosure some notable defects, such as high energy consumption, significant greenhouse gas emissions, hazardous fume, and unpleasant odors [12]. In addition, the high-temperature requirement and relatively longer cooling periods somehow limit its applicability, especially for high volume roadways built with heat-sensitive polymer modified mixes. Adapting with the need to reduce resource consumption and environmental emissions, Warm Mix Asphalt (WMA) has gradually been acknowledged as a promising technology in the pavement structure. This technique includes several notable advantages in the environmental aspect as well as enhances the characteristics of asphalt binders and mixtures [4; 9; 12; 16; 17]. This technology can significantly reduce the temperature of asphalt mixtures for the mixing and compacting processes by lowering the viscosity and casing foaming in the asphalt binders. The energy required to produce WMA decreases emissions and odors from plants and hence creates a better working condition at the plant and the paving site [18; 19]. In this technique, emissions and energy consumption can be significantly reduced due to its necessary mix temperature lower 30-50 \({}^{\circ}\)C compared to conventional HMA [11; 16]. These features lead to numerous benefits in several respects of WMA productions, such as environmental (reduced factory emissions), cost-effective, production, and paving process (increased workability and compaction performance) [4; 16; 17; 20; 21]. So far, it is quite acceptable to state that WMA is a potential solution that can balance environmental and performance objectives [3]. Indeed, the changes in features of asphalt mixtures (HMA or SMA) in cases of HMA or WMA techniques can be reflected through several characteristics, including chemical, physic-mechanical properties, and durability [3; 14; 16; 17; 21]. Among these characteristics, dynamic modulus, \(|\mathbb{E}^{\bullet}|\), has not only been increasingly acknowledged as a vital material property in mechanistic-empirical design and analysis [22] but has further reflected the pavement structures (e.g., strain and displacement) caused by loading rate and temperature [23; 24]. It is a primary property determined in the superpave simple performance method protocol that supplements the design of volumetric. Besides, the dynamic modulus \(|\mathbb{E}^{\bullet}|\) plays a vital role as an essential linear viscoelastic characteristic that can be employed in asphalt mixtures and pavements models [25]. In other words, the dynamic modulus \(|\mathbb{E}^{\bullet}|\) can be employed as a useful index to evaluate the effectiveness of the mixing technologies.