Cargando…

Learned Prediction of Compressive Strength of GGBFS Concrete Using Hybrid Artificial Neural Network Models

A new hybrid intelligent model was developed for estimating the compressive strength (CS) of ground granulated blast furnace slag (GGBFS) concrete, and the synergistic benefits of the hybrid algorithm as compared with a single algorithm were verified. While using the collected 269 data from previous...

Descripción completa

Detalles Bibliográficos
Autores principales: Han, In-Ji, Yuan, Tian-Feng, Lee, Jin-Young, Yoon, Young-Soo, Kim, Joong-Hoon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6888290/
https://www.ncbi.nlm.nih.gov/pubmed/31717660
http://dx.doi.org/10.3390/ma12223708
_version_ 1783475195251523584
author Han, In-Ji
Yuan, Tian-Feng
Lee, Jin-Young
Yoon, Young-Soo
Kim, Joong-Hoon
author_facet Han, In-Ji
Yuan, Tian-Feng
Lee, Jin-Young
Yoon, Young-Soo
Kim, Joong-Hoon
author_sort Han, In-Ji
collection PubMed
description A new hybrid intelligent model was developed for estimating the compressive strength (CS) of ground granulated blast furnace slag (GGBFS) concrete, and the synergistic benefits of the hybrid algorithm as compared with a single algorithm were verified. While using the collected 269 data from previous experimental studies, artificial neural network (ANN) models with three different learning algorithms namely back-propagation (BP), particle swarm optimization (PSO), and new hybrid PSO-BP algorithms, were constructed and the performance of the models was evaluated with regard to the prediction accuracy, efficiency, and stability through a threefold procedure. It was found that the PSO-BP neural network model was superior to the simple ANNs that were trained by a single algorithm and it is suitable for predicting the CS of GGBFS concrete.
format Online
Article
Text
id pubmed-6888290
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-68882902019-12-09 Learned Prediction of Compressive Strength of GGBFS Concrete Using Hybrid Artificial Neural Network Models Han, In-Ji Yuan, Tian-Feng Lee, Jin-Young Yoon, Young-Soo Kim, Joong-Hoon Materials (Basel) Article A new hybrid intelligent model was developed for estimating the compressive strength (CS) of ground granulated blast furnace slag (GGBFS) concrete, and the synergistic benefits of the hybrid algorithm as compared with a single algorithm were verified. While using the collected 269 data from previous experimental studies, artificial neural network (ANN) models with three different learning algorithms namely back-propagation (BP), particle swarm optimization (PSO), and new hybrid PSO-BP algorithms, were constructed and the performance of the models was evaluated with regard to the prediction accuracy, efficiency, and stability through a threefold procedure. It was found that the PSO-BP neural network model was superior to the simple ANNs that were trained by a single algorithm and it is suitable for predicting the CS of GGBFS concrete. MDPI 2019-11-10 /pmc/articles/PMC6888290/ /pubmed/31717660 http://dx.doi.org/10.3390/ma12223708 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Han, In-Ji
Yuan, Tian-Feng
Lee, Jin-Young
Yoon, Young-Soo
Kim, Joong-Hoon
Learned Prediction of Compressive Strength of GGBFS Concrete Using Hybrid Artificial Neural Network Models
title Learned Prediction of Compressive Strength of GGBFS Concrete Using Hybrid Artificial Neural Network Models
title_full Learned Prediction of Compressive Strength of GGBFS Concrete Using Hybrid Artificial Neural Network Models
title_fullStr Learned Prediction of Compressive Strength of GGBFS Concrete Using Hybrid Artificial Neural Network Models
title_full_unstemmed Learned Prediction of Compressive Strength of GGBFS Concrete Using Hybrid Artificial Neural Network Models
title_short Learned Prediction of Compressive Strength of GGBFS Concrete Using Hybrid Artificial Neural Network Models
title_sort learned prediction of compressive strength of ggbfs concrete using hybrid artificial neural network models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6888290/
https://www.ncbi.nlm.nih.gov/pubmed/31717660
http://dx.doi.org/10.3390/ma12223708
work_keys_str_mv AT haninji learnedpredictionofcompressivestrengthofggbfsconcreteusinghybridartificialneuralnetworkmodels
AT yuantianfeng learnedpredictionofcompressivestrengthofggbfsconcreteusinghybridartificialneuralnetworkmodels
AT leejinyoung learnedpredictionofcompressivestrengthofggbfsconcreteusinghybridartificialneuralnetworkmodels
AT yoonyoungsoo learnedpredictionofcompressivestrengthofggbfsconcreteusinghybridartificialneuralnetworkmodels
AT kimjoonghoon learnedpredictionofcompressivestrengthofggbfsconcreteusinghybridartificialneuralnetworkmodels