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Modelling and Predicting Self-Compacting High Early Age Strength Mortars Properties: Comparison of Response Models from Full, Fractioned and Small Central Composite Designs

2023Journal article Open access
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dc.contributor.authorCangussu, Nara
dc.contributor.authorMatos, Ana Mafalda
dc.contributor.authorMilheiro-Oliveira, Paula
dc.contributor.authorMaia, Lino
dc.date.accessioned2024-02-05T16:00:44Z
dc.date.available2024-02-05T16:00:44Z
dc.date.issued2023
dc.description.abstractThe mixture design of cement-based materials can be complex due to the increasing num ber of constituent raw materials and multiple requirements in terms of engineering performance and economic and environmental efficiency. Designing experiments based on factorial plans has shown to be a powerful tool for predicting and optimising advanced cement-based materials, such as self-compacting high-early-strength cement-based mortars. Nevertheless, the number of factor interactions required for factor scheduling increases considerably with the number of factors. Con sequently, the probability that the interactions do not significantly affect the answer also increases. As such, fractioned factorial plans may be an exciting option. For the first time, the current work compares the regression models and the predicting capacity of full, fractionated (A and B fractions) and small factorial designs to describe self-compacting high-early-strength cement-based mortars’ properties, namely, the funnel time, flexure and compressive strength at 24 h for the function of the mixture parameters Vw/Vc, Sp/p, Vw/Vp, Vs/Vm and Vfs/Vs for the different factorial designs. We combine statistical methods and regression analysis. Response models were obtained from the full, fractionated, and small plans. The full and fractionated models seem appropriate for describing the properties of self-compacting high-early-strength cement-based mortars in the experimental region. Moreover, the predicting ability of the full and fractionated factorial designs is very similar; however, the small design predictions reveal some concerns. Our results confirm the potentiality of fractioned plans to reduce the number of experiments and consequently reduce the cost and time of experimentation when designing self-compacting high-early-strength cement-based mortars.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationCangussu, N.; Matos, A.M.; Milheiro-Oliveira, P.; Maia, L. Modelling and Predicting Self-Compacting High Early Age Strength Mortars Properties: Comparison of Response Models from Full, Fractioned and Small Central Composite Designs. Appl. Sci. 2023, 13, 8413. https://doi.org/ 10.3390/app13148413pt_PT
dc.identifier.doi10.3390/app13148413pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/5530
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationInstitute of R&D in Structures and Construction
dc.relationNot Available
dc.relationCement-based composites shift to Industry 4.0: performance-based mix design methodology, assessment and quality control
dc.relationCentre of Mathematics of the University of Porto
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectDesign of experimentspt_PT
dc.subjectHigh strengthpt_PT
dc.subjectResponse modelpt_PT
dc.subjectSelf-compacting mortarpt_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Exatas e da Engenhariapt_PT
dc.titleModelling and Predicting Self-Compacting High Early Age Strength Mortars Properties: Comparison of Response Models from Full, Fractioned and Small Central Composite Designspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleInstitute of R&D in Structures and Construction
oaire.awardTitleNot Available
oaire.awardTitleCement-based composites shift to Industry 4.0: performance-based mix design methodology, assessment and quality control
oaire.awardTitleCentre of Mathematics of the University of Porto
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04708%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/CEEC INST 2018/CEECINST%2F00049%2F2018%2FCP1524%2FCT0001/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/CEEC IND4ed/2021.01765.CEECIND%2FCP1679%2FCT0004/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00144%2F2020/PT
oaire.citation.issue14pt_PT
oaire.citation.startPage8413pt_PT
oaire.citation.titleApplied Sciencespt_PT
oaire.citation.volume13pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStreamCEEC INST 2018
oaire.fundingStreamCEEC IND4ed
oaire.fundingStream6817 - DCRRNI ID
person.familyNameSerra Maia
person.givenNameLino Manuel
person.identifierR-000-97W
person.identifier.ciencia-idB711-823E-18C5
person.identifier.orcid0000-0002-6371-0179
person.identifier.scopus-author-id37051068400
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication3843bbca-e366-4c2f-ba3a-1b950a201d4b
relation.isAuthorOfPublication.latestForDiscovery3843bbca-e366-4c2f-ba3a-1b950a201d4b
relation.isProjectOfPublicationabd7f324-6515-4266-9436-ffcb0514f942
relation.isProjectOfPublicatione828512c-37f5-44e3-8abc-49733d0c863a
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