# Artificial Intelligence-Based Model for the Prediction of Dynamic Modulus of Stone Mastic Asphalt
## 1 Introduction
Various approaches have been, therefore, proposed for predicting the dynamic modulus \(|\mathbb{E}^{\bullet}|\) utilizing regression analysis based on laboratory measurements [7; 22; 26; 27; 28], modification of existing predictive equation in AASHTOWare [29], or Artificial Intelligence (AI) methods [25; 30; 31; 32; 33]. The most commonly used comprehensive and scientific approach, in terms of laboratory measurements, is related to a mechanistic approach, namely Mechanistic-Empirical Pavement Design Guide (MEPDG). This approach recommended the determination of the dynamic modulus at three levels [34]. However, the experimental part might not always be possible due to the lack of facilities or equipment. Besides, several models have been presented to overcome these difficulties, especially those proposed in the works of [PERSON] 1999 [35], [PERSON] 2006 [22], [PERSON] [36], and [PERSON] [37]. Even though these models have been roughly validated, the effectiveness is still being questioned, owing to the variation of predicted results for different asphalt mixtures [22; 38; 39; 40; 41; 42; 43; 44]. In addition, several studies have found that these models sometimes seem to overemphasize the effect of temperature, other mixture properties, or overpredict the dynamic modulus, for instance, in the works of [PERSON] [38], [PERSON] et al. [24], [PERSON] and [PERSON] [40], and [PERSON] et al. [42]. In several extreme conditions, i.e., high or low temperature, the [PERSON] 2006 and the Al-Khateeb models have also been found to overpredict the dynamic modulus [31; 45; 46; 47]. On the contrary, the [PERSON] model has been found to underpredict the dynamic modulus, as reported in the works of [PERSON] and [PERSON] [22], [PERSON] [38], [PERSON] et al. [42],and [PERSON] et al. [47]. These results showed that the prediction models of the expected dynamic modulus \(|\text{E}^{\bullet}|\) still need further clarification and investigation. Considering the modern approach, [PERSON] et al. [30] presented and compared a series of Artificial Neural Network (ANN) models with some existing models to predict the dynamic modulus \(|\text{E}^{\bullet}|\) of HMA. The ANN models indicated a notable accuracy compared with the conventional regression models and can be applied to the mechanistic-empirical pavement design guide. [PERSON] et al. [31] attempted to develop novel models to predict the dynamic modulus \(|\text{E}^{\bullet}|\) of HMA mixtures in the case of long-term pavement performance. The data consisted of measured moduli from many geographical places around the United States. The obtained results indicated that the predicted dynamic modulus \(|\text{E}^{\bullet}|\) utilizing ANN and measured dynamic modulus \(|\text{E}^{\bullet}|\) values were well matched. [PERSON] et al. [25] also proposed a fundamental modeling framework to predict dynamic modulus \(|\text{E}^{\bullet}|\) of HMA using viscoelastic principles combined with the physical and mechanical characteristics of the mixture. The proposed dynamic modulus models were verified as reliable and appropriate for a wide scope of temperatures and recommended in the AASHTO TP62-03 test protocol. Despite that, the application of a machine learning approach to SMA mixtures is still limited, and further elaboration of the dynamic modulus in function of the temperature and frequency is required. On the basis of sustainable development and environmental standpoints, the current study, therefore, aims at favoring the potential use of SMA with warm technology from two different angles. From an experimental point of view, dynamic modulus tests were performed to highlight the beneficial aspect of SMA using warm technology compared with the hot technology, as well as conventional dense-graded asphalt concretes. From a simulation angle, a novel machine learning algorithm was developed to accurately predict the desired mechanical property of the asphalt mixtures, and thus to facilitate the use in the design process or the construction and operation stages.