Compressive Strength Prediction of Fly Ash Concrete Using Machine Learning Techniques

被引:15
作者
Jiang, Yimin [1 ]
Li, Hangyu [2 ]
Zhou, Yisong [3 ]
机构
[1] Zhengzhou Univ Ind Technol, Sch Architecture & Civil Engn, Zhengzhou 451100, Peoples R China
[2] Southeast Univ, Sch Civil Engn, Nanjing 211189, Peoples R China
[3] Xinyang Coll, Sch Civil Engn, Xinyang 464000, Peoples R China
关键词
grid search; support vector regression; fly ash concrete; machine learning; random forest model; SUPPORT VECTOR MACHINE; REGRESSION;
D O I
10.3390/buildings12050690
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
It is time-consuming and uneconomical to estimate the strength properties of fly ash concrete using conventional compression experiments. For this reason, four machine learning models-extreme learning machine, random forest, original support vector regression (SVR), and the SVR model optimized by a grid search algorithm-were proposed to predict the compressive strength of fly ash concrete on 270 group datasets. The prediction results of the proposed model were compared using five evaluation indices, and the relative importance and effect of each input variable on the output compressive strength were analyzed. The results showed that the optimized hybrid model showed the best predictive behavior compared to the other three models, and can be used to forecast the compressive strength of fly ash concrete at a specific mix design ratio before conducting laboratory compression tests, which will save costs on the specimens and laboratory tests. Among the eight input variables listed, age and water were the two relatively most important features with superplasticizer and fly ash being of weaker relative importance.
引用
收藏
页数:16
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