Modelling of compressive strength of self-compacting concrete containing fly ash by gene expression programming

Abstract In the modelling study, two models are presented by gene expression programming (GEP) for estimation of compressive strength (f c ) of self-compacting concrete (SCC) produced with fly ash (FA). The main difference between two models is the number of heads determined in the development of m...

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Autor principal:	Özgür Deneme,İbrahim
Lenguaje:	English
Publicado:	Escuela de Construcción Civil, Pontificia Universidad Católica de Chile 2020
Materias:	Self-compacting concrete Fly ash Compressive strength Gene expression programming
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-915X2020000200346
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spelling	oai:scielo:S0718-915X20200002003462020-10-04Modelling of compressive strength of self-compacting concrete containing fly ash by gene expression programmingÖzgür Deneme,İbrahim Self-compacting concrete Fly ash Compressive strength Gene expression programming Abstract In the modelling study, two models are presented by gene expression programming (GEP) for estimation of compressive strength (f c ) of self-compacting concrete (SCC) produced with fly ash (FA). The main difference between two models is the number of heads determined in the development of models. Two established models are proposed to predict the f c values by utilizing the amount of cement, water, FA, coarse and fine aggregate, superplasticiser and age of specimen as input values for SCC mixtures. In the establishment of proposed models, 516 f c values are utilized. These values were obtained from 34 different published scientific experimental studies on the SCC produced with FA. The training and testing sets employed in the creation of models consist of 368 f c results of SCC mixtures. The models are validated with the remaining 148 f c results of SCC mixtures, which are not employed in training and testing sets. The estimated f c results attained from established models were compared with f c results of experimental studies, and previously proposed artificial neural network (ANN) model. These comparisons and the results of statistical evaluation have strongly revealed that the results of established models match well with the experimental results, and they are considered very reliable.info:eu-repo/semantics/openAccessEscuela de Construcción Civil, Pontificia Universidad Católica de ChileRevista de la construcción v.19 n.2 20202020-09-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-915X2020000200346en10.7764/rdlc.19.2.346
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Self-compacting concrete Fly ash Compressive strength Gene expression programming
spellingShingle	Self-compacting concrete Fly ash Compressive strength Gene expression programming Özgür Deneme,İbrahim Modelling of compressive strength of self-compacting concrete containing fly ash by gene expression programming
description	Abstract In the modelling study, two models are presented by gene expression programming (GEP) for estimation of compressive strength (f c ) of self-compacting concrete (SCC) produced with fly ash (FA). The main difference between two models is the number of heads determined in the development of models. Two established models are proposed to predict the f c values by utilizing the amount of cement, water, FA, coarse and fine aggregate, superplasticiser and age of specimen as input values for SCC mixtures. In the establishment of proposed models, 516 f c values are utilized. These values were obtained from 34 different published scientific experimental studies on the SCC produced with FA. The training and testing sets employed in the creation of models consist of 368 f c results of SCC mixtures. The models are validated with the remaining 148 f c results of SCC mixtures, which are not employed in training and testing sets. The estimated f c results attained from established models were compared with f c results of experimental studies, and previously proposed artificial neural network (ANN) model. These comparisons and the results of statistical evaluation have strongly revealed that the results of established models match well with the experimental results, and they are considered very reliable.
author	Özgür Deneme,İbrahim
author_facet	Özgür Deneme,İbrahim
author_sort	Özgür Deneme,İbrahim
title	Modelling of compressive strength of self-compacting concrete containing fly ash by gene expression programming
title_short	Modelling of compressive strength of self-compacting concrete containing fly ash by gene expression programming
title_full	Modelling of compressive strength of self-compacting concrete containing fly ash by gene expression programming
title_fullStr	Modelling of compressive strength of self-compacting concrete containing fly ash by gene expression programming
title_full_unstemmed	Modelling of compressive strength of self-compacting concrete containing fly ash by gene expression programming
title_sort	modelling of compressive strength of self-compacting concrete containing fly ash by gene expression programming
publisher	Escuela de Construcción Civil, Pontificia Universidad Católica de Chile
publishDate	2020
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-915X2020000200346
work_keys_str_mv	AT ozgurdeneme304brahim modellingofcompressivestrengthofselfcompactingconcretecontainingflyashbygeneexpressionprogramming
_version_	1714206308161290240

Modelling of compressive strength of self-compacting concrete containing fly ash by gene expression programming

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