A hybrid machine learning model to estimate self-compacting concrete compressive strength

被引：0

作者：

LY HaiBang ^{[1
]}

NGUYEN ThuyAnh ^{[1
]}

PHAM Binh Thai ^{[1
]}

NGUYEN May Huu ^{[1
,2
]}

机构：

[1] Civil Engineering Department, University of Transport Technology, Hanoi , Vietnam

[2] Civil and Environmental Engineering Program, Graduate School of Advanced Science and Engineering, Hiroshima University, Hiroshima -, Japan

来源：

Frontiers of Structural and Civil Engineering | 2022年 / 16卷 / 08期

关键词：

artificial neural network; grey wolf optimize algorithm; compressive strength; self-compacting concrete;

D O I：

暂无

中图分类号：

TU528 [混凝土及混凝土制品];

学科分类号：

0805 ; 080502 ; 081304 ;

摘要：

This study examined the feasibility of using the grey wolf optimizer (GWO) and artificial neural network (ANN) to predict the compressive strength (CS) of self-compacting concrete (SCC). The ANN-GWO model was created using 115 samples from different sources, taking into account nine key SCC factors. The validation of the proposed model was evaluated via six indices, including correlation coefficient (R), mean squared error, mean absolute error (MAE), IA, Slope, and mean absolute percentage error. In addition, the importance of the parameters affecting the CS of SCC was investigated utilizing partial dependence plots. The results proved that the proposed ANN-GWO algorithm is a reliable predictor for SCC’s CS. Following that, an examination of the parameters impacting the CS of SCC was provided.

引用

页码：990 / 1002

页数：13