## 1 Introduction  
Stone Matrix Asphalt (SMA), created in Germany during the 1960s [1], is a hot mixed asphalt which contains mainly a high binder content mortar and a coarse aggregate structure. The SMA maximizes stone-to-stone contact with an important concentration of coarse aggregate. The latter are held and bonded with a matrix of stabilizer and mineral filler in a thick asphalt layer [2]. The SMA has been widely used due to its many advantages such as high durability, high rut resistance, reduced noise pollution and resistance to reflective cracking [3; 4]. Several well-known disadvantages of the SMA can also be pointed out, for instance, drainage of binder or higher initial production costs compared to conventional hot-mix asphalt mixtures [5; 6]. However, many studies have demonstrated that despite the higher production cost, the durability of the SMA is better than that of conventional ones, thus selection becomes a cost-effectiveness analysis problem [7], making the need to carefully analyze the mechanical properties of the SMA highly required. Marshall parameters such as Marshall Stability (MS), Marshall Flow (MF) and Marshall Quotient (MQ) are important mechanical properties of the SMA, which directly reflect the performance of such asphalt concretes [6]. Indeed, the MS is an important property of the SMA as it indicates the performance of pavement subjected to showing and putting under usage conditions [8]. A good SMA stability depends essentially on internal friction--the interlocking as well as frictional resistance of aggregates--and the cohesion--a kind of binding force of the binder. The MF is, on the contrary, considered as an opposite property of the MS. It measures the reversible behavior of the wear course of pavements under traffic conditions [9]. Last but not least, the MQ, defined as a ratio of the MS and MF, indicates the resistance of asphalt mixtures to permanent deformation. The three Marshall parameters are widely used for the evaluation of resistance to displacement, distortion, rutting as well as shearing stresses of SMA [10; 11]. As the pavement is frequently subjected to traffic loads, it is very necessary to find an optimum manner to determine these parameters [7]. In general, these parameters are often determined by laboratory experiments or by traditional statistical approaches [7]. However, such procedures are complicated, cost and time consuming, and operator expertise is also required. Besides, various interesting parameters such as the specific gravity, air voids and voids in aggregates could be deduced directly by simple mathematical calculations if researchers can obtain these Marshall parameters by the help of another means. Apart from traditional laboratory experiments or traditional statistical analysis [12], the estimation of Marshall parameters could be approximated by another manner which has been recently investigated over the last three decades: Artificial Intelligence (AI) approaches. Indeed, AI simulations have been widely applied in many fields of structural engineering [13; 14; 15], civil engineering materials [16; 17; 18; 19; 20] as well as in pavement engineering due to their simplicity and effectiveness. The applications of AI in this field cover a broad range, such as pavement crack detection and classification [21], condition rating of jointed concrete pavements [22], prediction of the International Roughness Index (IRI) [23], evaluation of pavement conditions to deduce performance prediction models [24]. Regarding the mechanical properties of asphalt concrete, [PERSON] et al. [25; 26] applied Fuzzy Logic (FL) along with Artificial Neural Networks (ANN) to predict the relationship between the MS with related physical properties. The authors reported a better prediction capability of the AI models compared with classical statistical methods. In another attempt, [PERSON] et al. [27; 28] affirmed the use of Neural Networks (NN) to predict Marshall Test results and introduced the relationships in a closed form solution.