Open AccessPrediction of compressive strength of High-Performance Concrete by Random Forest algorithm
Author(s) -
Pengcheng Liu,
Xianguo Wu,
Hongyu Cheng,
Zheng Tiemei
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/552/1/012020
Subject(s) - random forest , compressive strength , range (aeronautics) , set (abstract data type) , computer science , random variable , algorithm , variable (mathematics) , optimization algorithm , compressed sensing , variables , sensitivity (control systems) , mathematical optimization , statistics , mathematics , machine learning , engineering , materials science , mathematical analysis , electronic engineering , composite material , programming language , aerospace engineering
The prediction results of the compressive strength of high-performance concrete (HPC) based on intelligent algorithms are seriously affected by the input variables. In this study, the Random Forest algorithm (RF) is introduced to optimize the number of input variables by evaluating the importance of influencing factors, and then predict the 28-day compressive strength through Random Forest Regression. The results show that this method is effective for the optimization of input variables, and when the parameters are set within a reasonable range, better prediction results can be obtained than non-variable optimization.