journal article·2022-10

Enhancing the properties of foam concrete 3D printing using porous aggregates

Kirubajiny Pasupathy, Sayanthan Ramakrishnan, Jay Sanjayan

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Abstract

The lightweight concrete (density <1000 kg/m3) is generally attained by introducing a large amount of air voids into fresh concrete for making so-called foam concrete. Such foam concrete is quite challenging in 3D concrete printing due to the high flowability of fresh mixes affecting the printability and foam stability during extrusion process. To overcome these limitations, this study investigates a combination of lightweight aggregates and premade foam in foam concrete to attain a density below 1000 kg/m3 for 3D printing applications. The expanded perlite (EP) aggregate was used as a replacement for fine sand that substantially reduced the foam content in the mix. The effect of EP on the fresh state properties such as rheology and printability as well as hardened properties including, mechanical properties, porosity and pore size distribution were investigated. It was demonstrated that the introduction of combined lightweight aggregate and foam has significantly improved the fresh and hardened properties of produced lightweight concrete. For instance, fresh foam concrete containing EP aggregate displayed high yield strength and apparent viscosity compared to the foam concrete without EP at similar densities. The compressive strength of 3D printed specimens containing EP was determined as 12.95 MPa, 15.5 MPa and 10.6 MPa in the perpendicular, longitudinal, and lateral directions respectively, compared to 5.5 MPa, 8.4 MPa and 4.2 MPa for the sand group at the similar density. Moreover, fine and regular pore size distribution was observed for 3D printed foam concrete with EP aggregate.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Innovations in Concrete and Construction Materials · Additive Manufacturing and 3D Printing Technologies · Bone Tissue Engineering Materials

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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