Evidence map›Paper›PMID 37049116›Full record

ArticleMaterials (Basel, Switzerland)2023

Tailoring Light-Weight Aggregates for Concrete 3D Printing Applications.

Yi Wei Daniel Tay, Ming Jen Tan, Teck Neng Wong

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Yi Wei Daniel TaySingapore Centre for 3D Printing, School of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore.ORCID 0000-0002-7369-2138
Ming Jen TanSingapore Centre for 3D Printing, School of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore.
Teck Neng WongSingapore Centre for 3D Printing, School of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Concrete 3D printing is a sustainable solution for manufacturing efficient designs and creating less waste, and selecting the optimal materials to use can amplify the advantages of this technology. In this study, we explore printing lightweight concrete by replacing normal weight aggregate with lightweight aggregates such as cenospheres, perlite, and foam beads. We adopt a systematic approach to investigate mixtures using different formulation methods such as the specific gravity and packing factor methods to improve the printing and mechanical performances of the mixtures. The rheological results showed significant improvement in the flow characteristics of the different mixtures using both the specific gravity method and the packing factor method to formulate the mixtures. Furthermore, a statistical tool was used to achieve optimal performance of the mixtures in terms of high specific compressive strength, high flow characteristics, and good shape retention capability by maximizing the specific compressive strength ratio, slump flow, and the static yield stress, while minimizing the slump, dynamic yield stress, and plastic viscosity. With the above design objectives, the optimal percentages of the aggregate replacements (cenosphere, perlite, and EPS foam beads) were 42%, 68%, and 44%, respectively. Finally, the optimized results also showed that the mixture with cenosphere aggregate replacement had the highest specific strength.

Indexed as

3D concrete printingadditive manufacturinglightweight materialspacking factorrheology

Identifiers

PMID37049116
PMCPMC10096486

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.