Additive construction of concrete deep beams using low-cost characterization methods and FEM-based topological optimization

dc.contributor.affiliationPontificia Universidad Católica del Perú. Departamento de Ingeniería
dc.contributor.affiliationPontificia Universidad Católica del Perú. Departamento de Ciencias
dc.contributor.authorSilva, G.
dc.contributor.authorQuispe, A.
dc.contributor.authorBaldoceda, J.
dc.contributor.authorKim, S.
dc.contributor.authorRuiz, G.
dc.contributor.authorPando, M.A.
dc.contributor.authorNakamatsu, J.
dc.contributor.authorAguilar, R.
dc.date.accessioned2026-03-13T16:57:51Z
dc.date.issued2024
dc.description.abstractAdditive manufacturing using concrete for large-scale construction purposes has demonstrated economic, social, and environmental benefits compared to conventional building procedures. These advantages stem from the capabilities of concrete 3D printing, which facilitates a rapid, accurate, and low-waste construction process with substantially less labor and energy requirements compared to traditional casting procedures such as formwork fabrication and stripping, concrete pouring, and concrete consolidation. This technology can pave the way for sustainable and cost-effective housing solutions when coupled with low-carbon concrete formulations and optimized structural designs. However, scientific and industrial experiences have shown that formulating printable concrete requires extensive testing and costly equipment to reach appropriate fresh and hardened-state properties. Therefore, accessible and practical mix-design protocols for the evaluation of printable concrete formulation are needed to enable in-situ control and broader adoption of 3D printing. Once a printable material is developed, innovative design methods, such as topology optimization, that exploit robot-controlled construction to fabricate efficient, safe, and free-form elements can be explored. In this context, this article presents a methodology based on a set of low-cost and accessible experimental tests to develop cement-based matrices with low binder content suitable for layer-by-layer deposition. Furthermore, a framework to design and fabricate efficient structural elements based on numerical-based topological optimization and concrete additive manufacturing is proposed and validated. The systematic experimental campaign carried out indicates that the yield strength obtained from shear vane tests, initially designed for geotechnical field tests, is a reliable reference value for proportioning extrudable, pumpable, and buildable concretes. Employing the proposed framework, four formulations with excellent printing capabilities are presented. These formulations are successfully utilized for additive manufacturing of a topologically optimized deep beam, achieving a remarkable 52% mass reduction compared to a solid element. This showcases the possibility of 3D printing structurally efficient elements with intricate geometries while minimizing material usage, all without the need for formworks.Principio del formulario.
dc.description.sponsorshipFunding: This work was supported by ProCiencia under the project: “Economía circular en la industria de la construcción con impresión 3D: Reuso de desechos de conchas de abanico, concreto de demolición y PET como agregados para construcción aditiva con concreto” (Contrato N° PE501079328–2022 ).
dc.identifier.doihttps://doi.org/10.1016/j.conbuildmat.2024.135418
dc.identifier.urihttp://hdl.handle.net/20.500.14657/205702
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofurn:issn:0950-0618
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.sourceConstruction and Building Materials; Vol. 418 (2024)
dc.subjectAdditive Manufacturing (AM)
dc.subjectAdditive Construction
dc.subject3D Concrete Printing (3DCP)
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#2.01.01
dc.titleAdditive construction of concrete deep beams using low-cost characterization methods and FEM-based topological optimization
dc.typehttp://purl.org/coar/resource_type/c_6501
dc.type.otherArtículo
dc.type.versionhttps://vocabularies.coar-repositories.org/version_types/c_970fb48d4fbd8a85/

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