# Data-Driven Prediction of Binder Rheological Performance in RAP/RAS-Containing Asphalt Mixtures
## 1 Introduction  
The economic and environmental incentives ultimately supported the growing utilization of recycled asphalt pavement (RAP) and recycled asphalt shingles (RAS) in asphalt mixtures. These include savings from construction waste and from virgin materials [1, 2, 3]. Heterogeneous and aged binders in RAP/RAS-containing mixtures may yield some potential performance deficiencies, such as stiffening and reduction of cracking resistance. Such issues jeopardize the longevity and resilience of pavements in cold conditions [4, 5, 6]. Therefore, the long-term sustainability of pavement is heavily dependent on better knowledge and prediction of the rheological properties of the binders extracted from different mixtures, including RAP and RAS. The rheological properties of the binder are sensitiveto the use of RAP/RAS in asphalt mixtures. Conventional binder testing methodologies, including rheological assessments, necessitate specific quantities that may be inadequate following recovery and extraction procedures, hence limiting binder characterization [5; 7]. This limitation may hinder a detailed characterization of the binder, as RAP/RAS comprises significantly aged binders with heterogeneous properties [8; 9]. Previous studies have shown that rheological and chemical properties significantly influence the evaluation of aged binders' performance [10; 11]. The incorporation of RAP/RAS into mixtures alters the chemical composition and thermal decomposition of the extracted binders. More aliphatic compounds are converted to aromatics, and more aging indices are chemically detected in extracted binders from mixtures with RAP/RAS [12; 13]. An increased level of thermal residue is generally indicative of a high asphaltene content, leading to a weakened fatigue life and lower resistance to thermal cracking of asphalt [12; 14; 15]. These alternations can be monitored using advanced techniques [i.e., Fourier transform infrared (FTIR) spectroscopy and thermogravimetric analysis (TGA)] on sub-milligram binders. Consequently, these methodologies offer an optimal alternative for analyzing characteristics when the binder is limited and a comprehensive understanding of aging and performance behavior is sought. Numerous challenges persist in predicting the rheological performance of binders subjected to chemical and thermal characterization. The main reason for this is the inter-composition complexity of the binder components [16; 17], nonlinear relationships among their properties, and complexity due to the high dimensionality of the data, which are more difficult to handle using conventional linear modeling. Data-driven techniques provide a good framework for processing this complexity. Machine learning is a specific example of these techniques. They extract correlations that can be predicted from different types of datasets. Random forest (RF) regression is an example of an ensemble model that handles complex interactions that are not linear between the input features and the targeted outcomes. They also do not assume functional relationships in advance. RF regression uses a parallel ensemble, which fits many decision trees simultaneously. Then, majority voting is used. This process reduces the overfitting problems. In addition, the procedure refines the precision of the predictions [18; 19]. These modeling procedures are best suited for studies with multiple independent variables, such as asphalt binder replacement (ABR) by RAP/RAS, asphalt content (AC), chemical indices, age of the mix, and thermal residue, interacting with the complex responses of rheological characteristics. They also provide interpretability tools, such as feature importance and correlation heatmaps, that can instruct users on the relative contribution of different predictors to outcome patterns and subsequent design decisions regarding materials [20].