Modeling the Slump Flow of Self-Compacting Concrete Incorporating Metakaolin Using Soft Computing Techniques

The sensitivity of slump flow of self-compacting concrete containing metakaolin to its ingredient materials and mixture proportions, necessitate the use of high accuracy models to guarantee both estimation and generalization features. Therefore this paper investigates the potential of multivariate a...

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Autores principales:	Ali Ashrafian, Mohammad Javad Taheri Amiri, farshidreza haghighi
Formato:	article
Lenguaje:	FA
Publicado:	Iranian Society of Structrual Engineering (ISSE) 2019
Materias:	self-compacting concrete slum flow metakaolin mars model tree modeling Bridge engineering TG1-470 Building construction TH1-9745
Acceso en línea:	https://doaj.org/article/ae11cc61e7f8419e8156f4b46ded3623
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id	oai:doaj.org-article:ae11cc61e7f8419e8156f4b46ded3623
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spelling	oai:doaj.org-article:ae11cc61e7f8419e8156f4b46ded36232021-11-08T15:52:14ZModeling the Slump Flow of Self-Compacting Concrete Incorporating Metakaolin Using Soft Computing Techniques2476-39772538-261610.22065/jsce.2018.90214.1243https://doaj.org/article/ae11cc61e7f8419e8156f4b46ded36232019-08-01T00:00:00Zhttps://www.jsce.ir/article_55214_bd73e06a9a442858c4875b5b37a0e248.pdfhttps://doaj.org/toc/2476-3977https://doaj.org/toc/2538-2616The sensitivity of slump flow of self-compacting concrete containing metakaolin to its ingredient materials and mixture proportions, necessitate the use of high accuracy models to guarantee both estimation and generalization features. Therefore this paper investigates the potential of multivariate adaptive regression splines (MARS) and model tree (MT) approaches in prediction of slump flow of self-compacting concrete. Total of 117 data collected from the several published literature were used in present work. The data used in proposed models are arranged in a format of eight input parameters including cement, coarse aggregate, fine aggregate, water, metakaolin, super plasticizer, binder and maximum size of aggregates (Dmax) and one output as slump flow. To evaluate the precision of the models, a comparative study has been performed in terms of RMSE, R and MAE indices. The results of training and testing datasets of the techniques are compared with experimental results and their comparisons demonstrate that the MARS and MT models have potential to predict concrete properties with great precision. Performed sensitivity analysis to assign effective parameters on slump flow was indicating fine aggregate and metakaolin is most effective variable for modeling and prediction in this type of the self-compacting concrete using MT technique in this studyAli AshrafianMohammad Javad Taheri Amirifarshidreza haghighiIranian Society of Structrual Engineering (ISSE)articleself-compacting concreteslum flowmetakaolinmarsmodel treemodelingBridge engineeringTG1-470Building constructionTH1-9745FAJournal of Structural and Construction Engineering, Vol 6, Iss شماره 2, Pp 5-20 (2019)
institution	DOAJ
collection	DOAJ
language	FA
topic	self-compacting concrete slum flow metakaolin mars model tree modeling Bridge engineering TG1-470 Building construction TH1-9745
spellingShingle	self-compacting concrete slum flow metakaolin mars model tree modeling Bridge engineering TG1-470 Building construction TH1-9745 Ali Ashrafian Mohammad Javad Taheri Amiri farshidreza haghighi Modeling the Slump Flow of Self-Compacting Concrete Incorporating Metakaolin Using Soft Computing Techniques
description	The sensitivity of slump flow of self-compacting concrete containing metakaolin to its ingredient materials and mixture proportions, necessitate the use of high accuracy models to guarantee both estimation and generalization features. Therefore this paper investigates the potential of multivariate adaptive regression splines (MARS) and model tree (MT) approaches in prediction of slump flow of self-compacting concrete. Total of 117 data collected from the several published literature were used in present work. The data used in proposed models are arranged in a format of eight input parameters including cement, coarse aggregate, fine aggregate, water, metakaolin, super plasticizer, binder and maximum size of aggregates (Dmax) and one output as slump flow. To evaluate the precision of the models, a comparative study has been performed in terms of RMSE, R and MAE indices. The results of training and testing datasets of the techniques are compared with experimental results and their comparisons demonstrate that the MARS and MT models have potential to predict concrete properties with great precision. Performed sensitivity analysis to assign effective parameters on slump flow was indicating fine aggregate and metakaolin is most effective variable for modeling and prediction in this type of the self-compacting concrete using MT technique in this study
format	article
author	Ali Ashrafian Mohammad Javad Taheri Amiri farshidreza haghighi
author_facet	Ali Ashrafian Mohammad Javad Taheri Amiri farshidreza haghighi
author_sort	Ali Ashrafian
title	Modeling the Slump Flow of Self-Compacting Concrete Incorporating Metakaolin Using Soft Computing Techniques
title_short	Modeling the Slump Flow of Self-Compacting Concrete Incorporating Metakaolin Using Soft Computing Techniques
title_full	Modeling the Slump Flow of Self-Compacting Concrete Incorporating Metakaolin Using Soft Computing Techniques
title_fullStr	Modeling the Slump Flow of Self-Compacting Concrete Incorporating Metakaolin Using Soft Computing Techniques
title_full_unstemmed	Modeling the Slump Flow of Self-Compacting Concrete Incorporating Metakaolin Using Soft Computing Techniques
title_sort	modeling the slump flow of self-compacting concrete incorporating metakaolin using soft computing techniques
publisher	Iranian Society of Structrual Engineering (ISSE)
publishDate	2019
url	https://doaj.org/article/ae11cc61e7f8419e8156f4b46ded3623
work_keys_str_mv	AT aliashrafian modelingtheslumpflowofselfcompactingconcreteincorporatingmetakaolinusingsoftcomputingtechniques AT mohammadjavadtaheriamiri modelingtheslumpflowofselfcompactingconcreteincorporatingmetakaolinusingsoftcomputingtechniques AT farshidrezahaghighi modelingtheslumpflowofselfcompactingconcreteincorporatingmetakaolinusingsoftcomputingtechniques
_version_	1718441693588488192

Modeling the Slump Flow of Self-Compacting Concrete Incorporating Metakaolin Using Soft Computing Techniques

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