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

Authors

• Jianglei Xing, Hohai University
• Xiao Tan, Hohai University
• Mu Guo, Shanghai Research Institute of Building Science Co
• Pengwei Guo, University College London
• Yuhuan Wang, University of Colorado Boulder
• Dongzhao Jin, Michigan Technological UniversityFollow

Document Type

Article

Publication Date

8-11-2026

Department

Department of Civil, Environmental, and Geospatial Engineering

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 R 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 R 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.

Publisher's Statement

Copyright: © 2026 by the authors. Licensee MDPI, Basel, Switzerland. Publisher’s version of record: https://doi.org/10.3390/ma19163408

Publication Title

Materials (Basel, Switzerland)

Recommended Citation

Xing, J., Tan, X., Guo, M., Guo, P., Wang, Y., & Jin, D. (2026). Machine Learning-Driven Prediction and Design Guidance for Asphalt Concrete Using Marshall Stability and Indirect Tensile Strength. Materials (Basel, Switzerland), 19(16). http://doi.org/10.3390/ma19163408
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p2/2944

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Version

Publisher's PDF