ArticleMaterials (Basel, Switzerland)2026
Interpretable Machine Learning for Predicting Splitting Strength of Asphalt Concrete: Insights from SHAP Analysis.
Article in Materials (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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Who cites it
3 citing papers in PubMed.
- Machine Learning-Driven Prediction and Design Guidance for Asphalt Concrete Using Marshall Stability and Indirect Tensile Strength.Materials (Basel, Switzerland) · 2026Article
- Improved BP Neural Network Ensemble Model for Asphalt Pavement Performance Prediction.Materials (Basel, Switzerland) · 2026Article
- An Overview and Perspectives on the Materials Genome Initiative-Based Asphalt Mix Design Framework.Materials (Basel, Switzerland) · 2026Review
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper proposes an interpretable machine learning approach for predicting the splitting strength of asphalt concrete and supporting data-driven mixture design. A database consisting of 296 samples was constructed, and 14 input variables related to asphalt properties, aggregate gradation, and fiber characteristics were selected for modeling. Eight machine learning models, namely TabPFN, ANN, SVR, RF, XGBoost, LightGBM, FLAML, and FT-Transformer, were developed and compared. The results show that all eight models achieved satisfactory predictive capability, whereas TabPFN overall achieved the best performance in the Monte Carlo cross-validation, with the lowest average RMSE of 0.34 ± 0.10, the lowest average MAE of 0.21 ± 0.02, a relatively low average MAD of 0.14 ± 0.03, the highest average R
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Registered trials
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