机器学习预测纤维增强高性能混凝土高温后的抗压强度
Explainable Machine Learning Analysis of Temperature Effects on Mechanical Strength of Fiber-Reinforced High-Performance Concrete
用 1204 条混凝土实验数据训练模型，XGBoost 预测高温后强度误差不到 4 MPa，还配了个 Python 小工具，材料方向的可以看看。
一项研究构建了基于 1,204 条实验记录的数据驱动框架，用于预测纤维增强高性能混凝土在温度作用下的抗压强度。研究对比了八种机器学习模型，其中 Gradient Boosting 和 XGBoost 在未见数据上均达到 R² 0.903。Gradient Boosting 的 RMSE 最低为 6.67 MPa，XGBoost 的 MAE 最低为 3.97 MPa、MAPE 为 10.60%。SHAP 与部分依赖分析显示温度是影响抗压强度的首要因素，其后依次为细骨料、减水剂、水泥用量和水胶比。表现最好的模型被部署到 Python 图形界面工具中以便快速预测。
Explainable Machine Learning Analysis of Temperature Effects on Mechanical Strength of Fiber-Reinforced High-Performance Concrete
Predicting the compressive strength of fiber-reinforced high-performance concrete subjected to temperature exposure is challenging because to the intricate interdependencies of mixture composition, fiber properties, curing conditions, and temperature. A data-driven framework to predict compressive strength utilizing 1,204 experimental records containing a diverse array of material designs and exposure situations. This study evaluates eight machine-learning models and demonstrate that ensemble approaches routinely surpass individual learners. Both gradient boosting and extreme gradient boosting exhibit superior performance on unseen data, each attaining a R^2 of 0.903. Gradient boosting produces the minimal root mean square error (6.67 MPa), whereas extreme gradient boosting achieves the lowest mean absolute error (3.97 MPa) and mean absolute percentage error (10.60%). The models are further interpreted utilizing Shapley additive explanations and partial dependence analysis, which reveal temperature as the primary factor influencing compressive strength, succeeded by fine aggregate, superplasticizer, cement content, water-to-binder ratio, and water content. Strength diminishes significantly with rising temperature, albeit the extent of deterioration is contingent upon the specific combination design. The best performing models are subsequently implemented in a Python-based graphical user interface for expedited prediction. Our findings offer a precise and comprehensible methodology for evaluating temperature-dependent compressive strength and facilitate the practical, data-driven design of fiber-reinforced high-performance concrete.
