# Data-Driven Prediction of Binder Rheological Performance in RAP/RAS-Containing Asphalt Mixtures
## 1 Introduction
The prediction power of machine learning models, including decision trees and RF, has been used in various aspects of asphalt applications, such as roughness [21], rutting of asphalt mixes, including RAP [22], and optimum AC [23]. Decision tree-based Algorithms were used to predict the mechanical performance of asphalt mixtures containing RAP and demolition waste materials [24]. This study highlighted the importance of RAP content in the prediction process [24]. Another study [25] highlighted the importance of RAP content on the moisture susceptibility of asphalt mixtures using XGBoost models. Mixture stiffness was predicted using artificial neural networks with a high influence of RAS content on the dynamic modulus of the mixture [26]. Another study [27] conceptualized an ensemble machine-learning model to tackle the rutting and fatigue predictions of mixtures with RAP, citing binder content, asphalt grade, and RAP content as major predictor parameters. The main concern of these studies was the use of machine learning to predict the performance of asphalt mixtures. In contrast, little consideration has been given to the performance assessment of the extracted binder, particularly its evaluation through chemical and thermal analyses and their correlations with rheological properties. Recent studies have explored the correlations between the rheological, thermal, and chemical properties of extracted binders from mixtures, including RAP/RAS [5; 12; 15; 28]. The thermal residue and percentages of light-combusted components (LCC) during the pyrolysis of asphalt binders were correlated with the rutting parameter (\(G^{\star}\)/\(\sin\!\delta\)). The extracted binders with fewer aged components resulting from the lower RAP/RAS binder replacements contained higher LCC percentages and lower thermal residues and asphaltene content and exhibited lower \(G^{\star}\)/\(\sin\!\delta\) values, diminishing the rutting resistance [5]. Having the highest aging components from RAP/RAS binder replacements, the extracted binder showed the highest fatigue cracking value (\(G^{\star}\).\(\sin\!\delta\)), thereby diminishing the binder's resistance to fatigue cracking, which coincided with the highest thermal residue percentage, the highest carbonyl index, the highest aromatic index, and the lowest aliphatic index [12; 15]. FTIR indices were correlated to the surface free energy of binder blends containing the RAP binder. Increasing the RAP binder reduced the surface free energy, likely because of the higher asphaltene content. However, the study [28] did not establish a clear correlation between the rheological and chemical properties of the binders. These previous studies pioneered the scope of the current study; however, they relied on bivariate correlations. The present study broadened the previous scope by incorporating multiple binders extracted from diverse mixture conditions, including various binder substitutions of RAP/RAS, into the multivariate machine learning models. The main objective of this study was to present a comprehensive framework for predicting the rheological characteristics of asphalt binders extracted from RAP/RAS-containing asphalt mixtures. Machine learning models were developed, validated, and compared in this study. Statistical analyses were conducted to highlight the models' predictive power.