基于机器学习的透水混凝土耐磨性能预测

摘要/Abstract

摘要： 本研究旨在利用机器学习模型进行透水混凝土耐磨性能预测。收集了150组透水混凝土耐磨性能试验数据并构建了数据库,采用特征相关性分析确定了6个输入参数,分别为骨料最大粒径、水胶比、砂率、骨胶比、粉煤灰掺量和旋转圈数。利用多种机器学习算法(XGBoost、Gradient Boosting、AdaBoost、Decision Tree和Random Forest)建立了透水混凝土磨损率预测模型,通过决定系数(R²)、均方根误差(RMSE)和平均绝对误差(MAE)对模型性能进行表征。研究结果表明,Gradient Boosting模型在训练集和测试集上均具有较高的准确性和较小的预测误差,与现有理论模型的比较分析也证实了Gradient Boosting模型在预测透水混凝土磨损率方面的优势。研究成果可为透水混凝土的设计和应用提供参考,并有望降低相关工程的维护成本。

关键词: 透水混凝土, 耐磨性能, 磨损率, 机器学习, Gradient Boosting模型

Abstract: The aim of this study is to utilize machine learning models for the prediction of the abrasion resistance of pervious concrete. 150 sets of pervious concrete abrasion resistance test data were collected and a database was constructed. 6 input parameters were identified using feature correlation analysis, namely maximum aggregate size, water/binder ratio, sand ratio, aggregate/binder ratio, fly ash ratio and rotation circle. A variety of machine learning algorithms (XGBoost, Gradient Boosting, AdaBoost, Decision Tree and Random Forest) were used to establish prediction models for the abrasion ratio of pervious concrete, and the model performance was characterized by coefficient of determination (R²), root mean squared error (RMSE) and mean absolute error (MAE). The results show that the Gradient Boosting model exhibits high accuracy and small prediction error on both the training and test sets, and the comparative analysis with the existing theoretical models confirms the advantages of the Gradient Boosting model in predicting the abrasion ratio of pervious concrete. The research results can provide a reference for the design and application of pervious concrete, and are expected to reduce the maintenance cost of related projects.

Key words: pervious concrete, abrasion resistance, abrasion ratio, machine learning, Gradient Boosting model

中图分类号:

TU528

白涛, 罗小宝, 邢国华. 基于机器学习的透水混凝土耐磨性能预测[J]. 硅酸盐通报, 2024, 43(1): 138-146.

BAI Tao, LUO Xiaobao, XING Guohua. Prediction of Abrasion Resistance of Pervious Concrete Based on Machine Learning[J]. BULLETIN OF THE CHINESE CERAMIC SOCIETY, 2024, 43(1): 138-146.

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