用可解释机器学习预测沥青混凝土抗拉强度，助力材料设计优化
Interpretable machine learning for predicting splitting strength of asphalt concrete: insights from SHAP analysis
- 构建6种模型对比，TabPFN表现最佳，误差低至0.28
- 揭示9个关键变量贡献率达92%，明确最优参数范围
- 开发可视化平台，让非专家也能理解模型决策
本文提出一种可解释的机器学习框架，用于预测沥青混凝土的抗拉强度（ST），支持数据驱动的混合料设计。建立包含296个样本的数据集，选取14个与沥青性能、集料级配和纤维特性相关的输入变量进行建模。比较了六种机器学习模型：TabPFN、ANN、SVR、RF、XGBoost和LightGBM。其中五种模型通过NSGA-II进行超参数优化，而TabPFN直接使用默认配置。结果表明，所有模型均具备良好预测能力，其中TabPFN在测试集上表现最优，均方根误差（RMSE）为0.28，平均绝对误差（MAE）为0.21，平均绝对百分比误差（MAPE）为18.01%，中位绝对偏差（MAD）为0.14，决定系数（R²）达0.88，综合得分最高为0.91。SHAP分析显示，九个主导变量合计贡献率达92.0%，其中Ag9.5、FT、Ag4.75、AC和Du影响最显著。此外，量化得出提升ST的有利参数范围：Ag9.5 < 66.8%、Ag4.75 < 45.0%、AC < 5.4 wt.%、AV < 3.6%、Du > 134.7 cm。最后，开发了一个集成预测与SHAP解释的图形化用户界面（GUI）平台，提升框架的可用性与实用性。
原文摘要 · Abstract (English)
This paper presents an interpretable machine-learning framework for predicting the splitting strength (ST) of asphalt concrete and supporting data-driven mixture design. A database consisting of 296 samples was established, and 14 input variables related to asphalt properties, aggregate gradation, and fiber characteristics were selected for modeling. Six machine-learning models, namely TabPFN, ANN, SVR, RF, XGBoost, and LightGBM, were developed and compared. Hyperparameter optimization was performed for five models using NSGA-II, while TabPFN was directly applied with its default configuration. The results show that all six models achieved satisfactory predictive capability, whereas TabPFN delivered the best overall performance on the testing set, with the lowest RMSE of 0.28, MAE of 0.21, MAPE of 18.01%, MAD of 0.14, the highest R^2 of 0.88, and the highest composite score of 0.91. SHAP analysis further revealed that nine dominant variables accounted for 92.0% of the total average contribution, among which Ag9.5, FT, Ag4.75, AC, and Du were the most influential. In addition, favorable parameter ranges for improving ST were quantified, such as Ag9.5 < 66.8%, Ag4.75 < 45.0%, AC < 5.4 wt.%, AV < 3.6%, and Du > 134.7 cm. Finally, a GUI platform integrating prediction and SHAP-based explanation was developed to improve the accessibility and practical applicability of the proposed framework.
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