用机器学习精准预测钢-聚丙烯纤维混凝土强度，提升工程设计效率。
Mechanical Strength Prediction of Steel-Polypropylene Fiber-based High-Performance Concrete Using Hybrid Machine Learning Algorithms
- 融合多种算法的混合模型预测混凝土强度，兼顾精度与可解释性。
- 最高预测准确率达99.4%（抗压强度），误差仅13%-30%左右。
- 识别出纤维比例和硅灰是影响强度的关键因素，适合材料优化参考。
本研究构建并评估了多种机器学习模型，用于预测钢-聚丙烯纤维增强型高强混凝土（HPC）的力学性能。考察了三种模型组合：额外树与XGBoost（ET-XGB）、随机森林与LightGBM（RF-LGBM）、Transformer与XGBoost（Transformer-XGB）。目标属性包括抗压强度（CS）、抗弯强度（FS）和抗拉强度（TS），基于大量已发表实验数据集训练。模型采用k折交叉验证、超参数优化、SHAP可解释性分析及不确定性评估，确保结果稳健可靠。其中，ET-XGB模型整体表现最优，测试R²值达0.994（CS）、0.944（FS）和0.978（TS），且抗压与抗拉强度的不确定性最低（约13–16%和30.4%）。RF-LGBM在抗弯强度预测中最为稳定（R²=0.977），不确定性最小（约5–33%）。Transformer-XGB虽具备较强预测能力（TS R²=0.978，FS R²=0.967），但始终具有最高不确定性，泛化能力较弱。SHAP分析表明，纤维长径比（AR1、AR2）、硅灰（Sfu）和钢纤维含量（SF）为最强预测因子，而水含量（W）和水胶比（w/b）则持续呈现负向影响。研究证实，机器学习可实现对HPC力学性能的高精度、可解释且泛化能力强的预测，为混凝土配比优化与结构性能评估提供有效工具。
原文摘要 · Abstract (English)
This research develops and evaluates machine learning models to predict the mechanical properties of steel-polypropylene fiber-reinforced high-performance concrete (HPC). Three model families were investigated: Extra Trees with XGBoost (ET-XGB), Random Forest with LightGBM (RF-LGBM), and Transformer with XGBoost (Transformer-XGB). The target properties included compressive strength (CS), flexural strength (FS), and tensile strength (TS), based on an extensive dataset compiled from published experimental studies. Model training involved k-fold cross-validation, hyperparameter optimization, Shapley additive explanations (SHAP), and uncertainty analysis to ensure both robustness and interpretability. Among the tested approaches, the ET-XGB model achieved the highest overall accuracy, with testing R^2 values of 0.994 for CS, 0.944 for FS, and 0.978 for TS and exhibited lowest uncertainty for CS and TS (approximately 13-16% and 30.4%, respectively). The RF-LGBM model provided the most stable and reliable predictions for FS (R^2 0.977), yielding the lowest uncertainty for FS (approximately 5-33%). The Transformer-XGB model demonstrated strong predictive capability (R^2 0.978 for TS and 0.967 for FS) but consistently showed the highest uncertainty, indicating reduced generalization reliability. SHAP analysis further indicated that fiber aspect ratios (AR1 and AR2), silica fume (Sfu), and steel fiber content (SF) were the most influential predictors of strength, whereas water content (W) and the water-binder ratio (w/b) consistently had negative effects. The findings confirm that machine learning models can provide accurate, interpretable, and generalizable predictions of HPC mechanical properties. These models offer valuable tools for optimizing concrete mix design and enhancing structural performance evaluation in engineering applications.
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