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Exploring Design Optimization of Self-Compacting Mortars with Response Surface Methodology

2023Journal article Open access
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dc.contributor.authorRocha, Stéphanie
dc.contributor.authorAscensão, Guilherme
dc.contributor.authorMaia, Lino
dc.date.accessioned2024-02-06T10:02:38Z
dc.date.available2024-02-06T10:02:38Z
dc.date.issued2023
dc.description.abstractThe ever-evolving construction sector demands technological developments to provide consumers with products that meet stringent technical, environmental, and economic requirements. Self-compacting cementitious mixtures have garnered significance in the construction market due to their enhanced compaction, workability, fluidity, and mechanical properties. This study aimed to harness the potential of statistical response surface methodology (RSM) to optimize the fresh proper ties and strength development of self-compacting mortars. A self-compacting mortar repository was used to build meaningful and robust models describing D-Flow and T-Funnel results, as well as the compressive strength development after 24 h (CS24h) and 28 days (CS28d) of curing. The quantitative input factors considered were A (water/cement), B (superplasticizer/powder), C (water/powder), and D (sand/mortar), and the output variables were Y1 (D-Flow), Y2 (T-Funnel), Y3 (CS24h), and Y4 (CS28d). The results found adjusted response models, with significant R2 values of 87.4% for the D-Flow, 93.3% for the T-Funnel, and 79.1% for the CS24h. However, for the CS28d model, a low R2 of 39.9% was found. Variable A had the greatest influence on the response models. The best correlations found were between inputs A and C and outputs Y1 and Y2, as well as input factors A and D for responses Y3 and Y4. The resulting model was enhanced, thereby resulting in a global desirability of approximately 60%, which showcases the potential for the further refinement and optimization of RSM models applied to self-compacting mortars.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citation: Rocha, S.; Ascensão, G.; Maia, L. Exploring Design Optimization of Self-Compacting Mortars with Response Surface Methodology. Appl. Sci. 2023, 13, 10428. https://doi.org/ 10.3390/app131810428pt_PT
dc.identifier.doi10.3390/app131810428pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/5532
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationUIDB/ECI/04450/2020pt_PT
dc.relationInstitute of R&D in Structures and Construction
dc.relationNot Available
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectSelf-compacting mortarspt_PT
dc.subjectDesign of experimentspt_PT
dc.subjectFresh propertiespt_PT
dc.subjectCompressive strengthpt_PT
dc.subjectANOVApt_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Exatas e da Engenhariapt_PT
dc.titleExploring Design Optimization of Self-Compacting Mortars with Response Surface Methodologypt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleInstitute of R&D in Structures and Construction
oaire.awardTitleNot Available
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.citation.issue18pt_PT
oaire.citation.startPage10428pt_PT
oaire.citation.titleApplied Sciencespt_PT
oaire.citation.volume13pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStreamCEEC INST 2018
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.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
relation.isProjectOfPublication.latestForDiscoverye828512c-37f5-44e3-8abc-49733d0c863a

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