Machine Learning-Driven Prediction and Design Guidance for Asphalt Concrete Using Marshall Stability and Indirect Tensile Strength

Abstract

This work addresses the simultaneous prediction of Marshall Stability (MS) and Indirect Tensile Strength (ITS) by integrating machine learning models with multi-objective optimization for the preliminary design of asphalt concrete. Based on 389 experimental samples, 15 variables were selected to describe asphalt properties, aggregate gradation, volumetric parameters and fiber characteristics, and four dual-output prediction models were developed. The models were evaluated using 50 Monte Carlo splits. TabICLv2 performed slightly better for MS prediction, with an RMSE of 1.49 ± 0.22 kN and an R2 of 0.85 ± 0.04, whereas TabPFN showed a slight advantage for ITS prediction, achieving an RMSE of 0.23 ± 0.08 MPa and an R2 of 0.91 ± 0.06. Furthermore, Pareto filtering identified nine non-dominated mixtures, and TOPSIS ranking selected the highest-ranked equal-weight compromise mixture, with MS = 15.23 kN and ITS = 3.90 MPa. The results indicate that mineral fibers are more suitable for improving the balanced performance of MS and ITS, carbon fibers are more favorable for improving MS, and plastic fibers are more effective in improving ITS. Finally, a Streamlit-based graphical user interface was developed to enable real-time prediction and MS–ITS trade-off visualization, providing a reference for preliminary mix design of asphalt concrete.