摘要
砂浆流变性除了与混合料成分特性和配合比设计相关外,还随胶凝材料混合时间的长短而发生改变。为研究上述因素对砂浆流变性能的影响,采用流变仪获得4个不同时段、30种不同配合比下砂浆的塑性黏度与屈服应力,并对得到的120组流变性能数据进行数据清洗等处理分析工作。接着通过支持向量回归、K-邻近回归和随机森林回归3种机器学习算法,以水泥含量、机制砂、粉煤灰、石灰石矿粉、减水剂、水、砂浆混合后时间、水灰比、骨胶比以及水胶比等参数作为自变量,塑性黏度与屈服应力作为因变量,对砂浆的流动性能进行学习预测。最终结果是支持向量回归算法对砂浆的塑性黏度与屈服应力预测准确率最高,预测结果的平均绝对误差、均方根误差、平均绝对百分比误差以及决定系数均优于其他模型,拥有更好的泛化能力,在预测领域中具有良好的适用性。通过时间效应分析,得到了不同组分对水泥砂浆流变性能的重要系数,其中减水剂、水泥、砂、水对于砂浆的流变性能影响比较显著。最后使用支持向量回归对具有较大特征重要性的变量(水泥、机制砂以及减水剂)进行单一特征分析,通过改变单一变量得到了该变量对砂浆流变性能的影响曲线。
Mortar rheological properties are not only influenced by the mixture component characteristics and mix design,but also varying with the duration of mixing with cementitious materials.To study the influence of these factors on mortar rheological performance,the rheometer was used to obtain the plastic viscosity and yield stress of mortar at 4 different time intervals,and with 30 different mix ratios.The generated 120 sets of rheological performance data were cleaned and analyzed.Subsequently,3 machine learning algorithms of support vector regression,K-nearest neighbor regression and random forest regression were used to predict the mortar flow properties.Parameters(e.g.,cement content,manufactured sand,fly ash,limestone powder,water-reducing agents,water,time after mortar mixing,water-cement ratio,aggregate-cement ratio,and water-cement ratio)were used as independent variables.The plastic viscosity and yield stress were as dependent variables.The result indicates that the support vector regression algorithm has the highest accuracy in predicting plastic viscosity and yield stress of mortar.The MAE,RMSE,MAPE and R^(2)show better generalization capability and applicability in the prediction field compared to other models.Through time-effect analysis,the significant coefficients for influence of different components on mortar rheological properties are identified.The water-reducing agents,cement,sand,and water have significant influence on mortar rheological properties.Finally,the single-feature analysis is conducted by using support vector regression on the variables with high feature importance(e.g.,cement,manufactured sand and water-reducing agents).The influencing curves of these variables on mortar rheological properties are obtained by varying each variable individually.
作者
蔡锦程
许子彦
董振勇
徐荣桥
赵阳
CAI Jin-cheng;XU Zi-yan;DONG Zhen-yong;XU Rong-qiao;ZHAO Yang(College of Civil Engineering and Architecture,Zhejiang University,Hangzhou,Zhejiang 310058,China;ZCCC Road and Bridge Construction Co.,Ltd.,Hangzhou,Zhejiang 310051,China;Zhejiang Provincial Engineering Research Center for Digital and Smart Maintenance of Highway,Hangzhou,Zhejiang 310051,China)
出处
《公路交通科技》
CAS
CSCD
北大核心
2024年第6期138-147,共10页
Journal of Highway and Transportation Research and Development
基金
浙江大学-浙江交工协同创新联合研究中心项目(ZDJG2021006)。
关键词
桥梁工程
流变性能
机器学习
砂浆
时间
预测分析
bridge engineering
rheological property
machine learning
mortar
time
predictive analysis
