Predicting the compressive strength of self-compacting concrete using Elman artificial neural network with two different sets of input parameters

In recent years, artificial neural networks converted from a theoretical approach to the widely-used technology with successful applications to different problems. In fact, artificial neural networks are a powerful tool that give appropriate solutions to problems which are difficult to solve through...

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Autores principales:	Atefeh Gholamzadeh Chitgar, Javad Berenjian
Formato:	article
Lenguaje:	FA
Publicado:	Iranian Society of Structrual Engineering (ISSE) 2019
Materias:	self compacting concrete prediction compressive strength neural network input Bridge engineering TG1-470 Building construction TH1-9745
Acceso en línea:	https://doaj.org/article/da163dab9cc5401aace7e208fe308f59
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spelling	oai:doaj.org-article:da163dab9cc5401aace7e208fe308f592021-11-08T15:50:45ZPredicting the compressive strength of self-compacting concrete using Elman artificial neural network with two different sets of input parameters2476-39772538-261610.22065/jsce.2017.83926.1161https://doaj.org/article/da163dab9cc5401aace7e208fe308f592019-02-01T00:00:00Zhttps://www.jsce.ir/article_87078_05746e86d1b1438c5370e1e6edfa21e8.pdfhttps://doaj.org/toc/2476-3977https://doaj.org/toc/2538-2616In recent years, artificial neural networks converted from a theoretical approach to the widely-used technology with successful applications to different problems. In fact, artificial neural networks are a powerful tool that give appropriate solutions to problems which are difficult to solve through conventional techniques. Nowadays, these networks, which are inspired by the biological nervous system, are also extensively used to solve a wide range of complex problems in civil engineering. The purpose of the current study is a performance evaluation of the Elman artificial neural networks with various input parameters in order to predict the compressive strength of Self Compacting Concrete (SCC). Therefore, once, 8 effective parameters and next, in order to simulate a real experimental conditions, 140 parameters were entered as inputs in the Elman neural networks. According to the results, Elman neural networks, as a reliable tool, have high strength for predicting the desired properties along with saving time and cost. In addition, in both 7 and 28-day compressive strength, the constructed networks with 140 input parameters compared to ones with 8, have 74.54 and 70.44 percent improvement respectively regarding their test errors. The effective inputs straightly affect the networks errors in the prediction of the desired properties.Atefeh Gholamzadeh ChitgarJavad BerenjianIranian Society of Structrual Engineering (ISSE)articleself compacting concretepredictioncompressive strengthneural networkinputBridge engineeringTG1-470Building constructionTH1-9745FAJournal of Structural and Construction Engineering, Vol 5, Iss 4, Pp 162-178 (2019)
institution	DOAJ
collection	DOAJ
language	FA
topic	self compacting concrete prediction compressive strength neural network input Bridge engineering TG1-470 Building construction TH1-9745
spellingShingle	self compacting concrete prediction compressive strength neural network input Bridge engineering TG1-470 Building construction TH1-9745 Atefeh Gholamzadeh Chitgar Javad Berenjian Predicting the compressive strength of self-compacting concrete using Elman artificial neural network with two different sets of input parameters
description	In recent years, artificial neural networks converted from a theoretical approach to the widely-used technology with successful applications to different problems. In fact, artificial neural networks are a powerful tool that give appropriate solutions to problems which are difficult to solve through conventional techniques. Nowadays, these networks, which are inspired by the biological nervous system, are also extensively used to solve a wide range of complex problems in civil engineering. The purpose of the current study is a performance evaluation of the Elman artificial neural networks with various input parameters in order to predict the compressive strength of Self Compacting Concrete (SCC). Therefore, once, 8 effective parameters and next, in order to simulate a real experimental conditions, 140 parameters were entered as inputs in the Elman neural networks. According to the results, Elman neural networks, as a reliable tool, have high strength for predicting the desired properties along with saving time and cost. In addition, in both 7 and 28-day compressive strength, the constructed networks with 140 input parameters compared to ones with 8, have 74.54 and 70.44 percent improvement respectively regarding their test errors. The effective inputs straightly affect the networks errors in the prediction of the desired properties.
format	article
author	Atefeh Gholamzadeh Chitgar Javad Berenjian
author_facet	Atefeh Gholamzadeh Chitgar Javad Berenjian
author_sort	Atefeh Gholamzadeh Chitgar
title	Predicting the compressive strength of self-compacting concrete using Elman artificial neural network with two different sets of input parameters
title_short	Predicting the compressive strength of self-compacting concrete using Elman artificial neural network with two different sets of input parameters
title_full	Predicting the compressive strength of self-compacting concrete using Elman artificial neural network with two different sets of input parameters
title_fullStr	Predicting the compressive strength of self-compacting concrete using Elman artificial neural network with two different sets of input parameters
title_full_unstemmed	Predicting the compressive strength of self-compacting concrete using Elman artificial neural network with two different sets of input parameters
title_sort	predicting the compressive strength of self-compacting concrete using elman artificial neural network with two different sets of input parameters
publisher	Iranian Society of Structrual Engineering (ISSE)
publishDate	2019
url	https://doaj.org/article/da163dab9cc5401aace7e208fe308f59
work_keys_str_mv	AT atefehgholamzadehchitgar predictingthecompressivestrengthofselfcompactingconcreteusingelmanartificialneuralnetworkwithtwodifferentsetsofinputparameters AT javadberenjian predictingthecompressivestrengthofselfcompactingconcreteusingelmanartificialneuralnetworkwithtwodifferentsetsofinputparameters
_version_	1718441705288499200

Predicting the compressive strength of self-compacting concrete using Elman artificial neural network with two different sets of input parameters

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