# Data-Driven Prediction of Binder Rheological Performance in RAP/RAS-Containing Asphalt Mixtures
## 4 Conclusions  
Understanding and optimizing the performance of RAP/RAS-containing asphalt binders would require developing a comprehensive predictive framework, validating the models developed from machine learning, and selecting the key features that influence binders' performance. Linear and RF nonlinear machine learning models were developed and validated in this study based on a framework incorporating the chemical and thermal properties of the binders and the descriptive information of the RAP/RAS-containing mixtures. The proposed framework is an important step-by-step guide for the pavement industry to enhance the performance of asphalt mixes based on sustainable asphalt recycling principles. The following principal conclusions were drawn:  
1. Robust predictive capability: Machine learning, specifically RF regression, enabled accurate predictions of binder performance at high, intermediate, and low temperatures. The RF nonlinear models reduced RMSE by 69% for rutting resistance, 37% for fatigue cracking, and 21% for thermal cracking when compared to linear model performance. In terms of block cracking, the RF model had an R value of 1.00, but it overfitted the data, resulting in an RMSE that was 41% greater than that obtained using a linear model. While all of these results demonstrate the importance of combining metrics of accuracy with residuals to assess model robustness, they are of concern, especially with a dataset that had such variability. 2. Practical for limited binder amounts: One advantage of the framework is its capability to provide reliable predictions using small amounts of extracted binder. This is beneficial in practice because field cores with high RAP/RAS contents usually yield limited amounts of extracted binders. 3. Enhanced knowledge of binder performance: The combination of rheological and chemical analyses, such as aging, aromatic and aliphatic indices, and thermal residue characteristics, enables the understanding of binder development in recycled systems. This is a way to successfully break through some of the constraints of conventional methodologies based exclusively on rheological measurements. 4. Feature importance emphasizes the dominance of chemical and thermal properties: Thermal residue, along with carbonyl, sulfoxide, aromatic, and aliphatic indices, consistently ranked among the top five most influential features affecting the rheological characteristics of the extracted binders. 5. Promoting sustainable recycling: The framework allocates data-driven information on the use of RAP/RAS, thus ensuring that the utilization of RAP/RAS is more sustainable. The study establishes a benchmark for balancing sustainability and performance by evaluating RAP/RAS binder replacement up to 35%. 6. Scalability, broader applications, and limitations: This study was conducted on mixtures with RAP/RAS; however, the framework can be applied to other recycled or modified binders. With further development, it could be expanded into a digital tool for quality control and mixture optimization in both research and field industries. Although the suggested framework offers accurate forecasts on binders extracted from mixtures with 0-35% RAP/RAS binder replacements, its application beyond this range requires recalibration and validation with expanded datasets. Conceptualization, E.D.-A. and M.A. ; methodology, E.D.-A. and M.A. ; software, E.D.-A. ; validation, E.D.-A. and M.A. ; formal analysis, E.D.-A. and M.A. ; investigation, E.D.-A. ; resources, M.A. ; data curation, E.D.-A. and M.A. ; writing--original draft preparation, E.D.-A. and M.A. ; writing--review and editing, E.D.-A. and M.A. ; visualization, E.D.-A. and M.A. ; supervision, M.A. ; project administration, M.A. ; funding acquisition, M.A. All authors have read and agreed to the published version of the manuscript.