Machine Learning–Based Prediction and Optimization of Balanced Mixture Design Performance Indices

Abstract

The balanced mix design (BMD) concept is an emerging methodology that facilitates the design of engineered asphalt mixtures. This approach is particularly beneficial for mixtures containing conventional and high reclaimed asphalt pavement, for which the traditional volumetric design methods may fail to effectively address the performance characteristics. However, given production variability, these engineered mixtures can still fail to meet the required thresholds. Additionally, identifying the cause of this imbalance is challenging. To maximize the benefits of BMD implementation, this study introduces machine learning (ML) algorithms including linear regression (LR), random forest (RF), extreme gradient boosting (XGB), and support vector regression (SVR) as strategic tools to predict mixtures’ BMD performance indices. 648 specimens fabricated for quality acceptance as part of the 2020 Virginia Accelerated Pavement Testing Program is used for the modeling and analysis. The durability, cracking, and rutting susceptibility of the specimens were evaluated using the Cantabro test, the indirect tensile cracking test (IDT-CT), and the asphalt pavement analyzer (APA) rut test. Key outcomes include: a) ML models, including RF, XGB, and SVR, demonstrated superior performance compared with LR; b) feature importance analysis from ML models identified dominant factors for each BMD test, also highlighting the reheating process; and c) a pseudo in situ deployment was simulated to optimize BMD implementation. The dimensionality reduction analysis—uniform manifold approximation and projection—highlighted the practical challenges associated with concurrently improving multiple performance metrics. Ultimately, the pivotal role of ML in advancing both the design and production phases was emphasized.