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Cited by 158

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

3D concrete printer parameter optimisation for high rate digital construction avoiding plastic collapse

Kruger, Jacques, Cho, Seung, Zeranka, Stephan, Viljoen, Celeste, van Zijl, Gideon·2020·Composites Part B: Engineering

Abstract

Abstract This research presents the development and application of a constructability design model, which determines the print speed and filament layer height combination that yields the fastest vertical building rate, whilst ensuring for the successful construction of an object. A 3D concrete printed structural wall element is used to validate the model. High variation in material rheological properties lead to an over prediction by the model if mean model parameter values are used. Consequently, a probabilistic design model is developed to reduce the impact of high variation in material properties on the accuracy of the deterministic design model. The first-order reliability method (FORM) is applied and material partial factors derived.

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Metadata

Authors
Kruger, J., Cho, S., Zeranka, S., Viljoen, C., & van Zijl, G.
Venue
Composites Part B: Engineering
Publisher
Elsevier BV
Published
2020-02
Volume
183
Pages
107660
ISSN
1359-8368 (ISSN-L)
OpenAlex
W2994455817
Cited by
158
References
1
FWCI
10.52
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: 3D concrete printer parameter optimisation for high rate digital construction avoiding plastic collapse. https://app.cochat.ai/papers/10.1016/j.compositesb.2019.107660
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Kruger, J., Cho, S., Zeranka, S., Viljoen, C., & van Zijl, G. (2020). 3D concrete printer parameter optimisation for high rate digital construction avoiding plastic collapse. Composites Part B: Engineering. https://doi.org/10.1016/j.compositesb.2019.107660

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.