eprintid: 13409
rev_number: 6
eprint_status: archive
userid: 564
dir: disk0/00/01/34/09
datestamp: 2020-06-23 13:09:26
lastmod: 2020-06-23 13:09:26
status_changed: 2020-06-23 13:09:26
type: article
metadata_visibility: show
creators_name: Ofuyatan, Olatokunbo M.
creators_name: Edeki, S.O.
title: Data set on predictive compressive strength
model for self-compacting concrete
ispublished: pub
subjects: TA
divisions: sch_civ
full_text_status: public
keywords: Predictivemodel
Compressivestrength
Watercementratio
Day-length
abstract: The determinationofcompressivestrengthisaffectedbymany
variablessuchasthewatercement(WC)ratio,thesuperplasticizer
(SP), theaggregatecombination,andthebindercombination.In
this datasetarticle,7,28,and90-daycompressivestrengthmodels
are derivedusingstatisticalanalysis.Theresponsesurfacemeth-
odology isusedtoinvestigatetheeffectoftheparameters:Varying
percentagesofash,cement,WC,andSPonhardenedproperties-
compressivestrengthat7,28and90days.Thelevelsofindependent
parametersaredeterminedbasedonpreliminaryexperiments.The
experimentalvaluesforcompressivestrengthat7,28and90days
and modulusofelasticityunderdifferenttreatmentconditionsare
also discussedandpresented.Thesedatasetcaneffectivelybeused
for modellingandpredictioninconcreteproductionsettings
date: 2018
date_type: published
publication: Data in Brief
volume: 17
publisher: Elsevier
pagerange: 801-806
id_number: https://doi.org/10.1016/j.dib.2018.02.008
refereed: TRUE
official_url: http://www.elsevier.com/locate/dib
citation:   Ofuyatan, Olatokunbo M. and Edeki, S.O.  (2018) Data set on predictive compressive strength model for self-compacting concrete.  Data in Brief, 17.  pp. 801-806.      
document_url: http://eprints.covenantuniversity.edu.ng/13409/1/1-s2.0-S2352340918301240-main.pdf