Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete

被引:91
|
作者
Chopra, Palika [1 ]
Sharma, Rajendra Kumar [1 ]
Kumar, Maneek [2 ]
Chopra, Tanuj [2 ]
机构
[1] Thapar Univ, Dept Comp Sci & Engn, Patiala, Punjab, India
[2] Thapar Univ, Dept Civil Engn, Patiala, Punjab, India
关键词
MULTIPLE LINEAR-REGRESSION; ARTIFICIAL NEURAL-NETWORK; FLY-ASH; SILICA FUME; MODEL;
D O I
10.1155/2018/5481705
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
A comparative analysis for the prediction of compressive strength of concrete at the ages of 28, 56, and 91 days has been carried out using machine learning techniques via "R" software environment. R is digging out a strong foothold in the statistical realm and is becoming an indispensable tool for researchers. The dataset has been generated under controlled laboratory conditions. Using R miner, the most widely used data mining techniques decision tree (DT) model, random forest (RF) model, and neural network (NN) model have been used and compared with the help of coefficient of determination (R-2) and root-mean-square error (RMSE), and it is inferred that the NN model predicts with high accuracy for compressive strength of concrete.
引用
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页数:9
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