Predicting the Compressive Strength of Environmentally Friendly Concrete Using Multiple Machine Learning Algorithms
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @Article{yang:2024:Buildings,
-
author = "Yanhua Yang and Guiyong Liu and Haihong Zhang and
Yan Zhang and Xiaolong Yang",
-
title = "Predicting the Compressive Strength of Environmentally
Friendly Concrete Using Multiple Machine Learning
Algorithms",
-
journal = "Buildings",
-
year = "2024",
-
volume = "14",
-
number = "1",
-
pages = "Article No. 190",
-
keywords = "genetic algorithms, genetic programming",
-
ISSN = "2075-5309",
-
URL = "https://www.mdpi.com/2075-5309/14/1/190",
-
DOI = "doi:10.3390/buildings14010190",
-
abstract = "Machine learning (ML) algorithms have been widely used
in big data prediction and analysis in terms of their
excellent data regression ability. However, the
prediction accuracy of different ML algorithms varies
between different regression problems and data sets. In
order to construct a prediction model with optimal
accuracy for fly ash concrete (FAC), ML algorithms such
as genetic programming (GP), support vector regression
(SVR), random forest (RF), extremely gradient boost
(XGBoost), backpropagation artificial neural network
(BP-ANN) and adaptive network-based fuzzy inference
system (ANFIS) were selected as regression and
prediction algorithms in this study; the particle swarm
optimisation (PSO) algorithm was also used to optimise
the structure and hyperparameters of each algorithm.
The statistical results show that the performance of
the assembled algorithms is better than that of an
NN-based algorithm. In addition, PSO can effectively
improve the prediction accuracy of the ML algorithms.
The comprehensive performance of each model is analysed
using a Taylor diagram, and the PSO-XGBoost model has
the best comprehensive performance, with R2 and MSE
equal to 0.9072 and 11.4546, respectively.",
-
notes = "also known as \cite{buildings14010190}",
- }
Genetic Programming entries for
Yanhua Yang
Guiyong Liu
Haihong Zhang
Yan Zhang
Xiaolong Yang
Citations