An Optimum Mix-Design Method for 3D Concrete Printing Applications (2023-06)¶

10.1007/978-3-031-33187-9_61

Sergis Vasileios, Ouellet-Plamondon Claudiane
Contribution - Proceedings of the International RILEM Conference on Synergising Expertise Towards Sustainability and Robustness of Cement-Based Materials and Concrete Structures, pp. 665-672

Abstract

Developing cement-based mortar mixes for three-dimensional printing applications is a challenging task with multiple competing objectives, especially with climate change adaptation. Mortar mix design is a laboratory method that determines the necessary quantities and types of cement, sand, chemical admixtures, and water to form a combination with the specified qualities. The influx of new ingredients to investigate can lead to an inefficient amount of labour. This research is part of a bigger project aiming to automate the development of mortar mixtures for three-dimensional concrete printing technology. A party optimizer suggests novel combinations by adjusting the ingredients and their proportions, whereas feed-forward neural networks predict their properties. In total, seven factors are investigated, five of which are quantitative and two qualitative. These factors include the type of cement and superplasticizer used, as well as the sand-to-binder ratio, water-to-binder ratio, and admixture doses. The initial set of mixes formed in the laboratory derived from a D-optimal set of 18 mixes. Tests frequently used in traditional construction are conducted to correlate them with important properties for 3D concrete printing applications. The flow table test correlates with flowability, whereas the slump test correlates with shape stability. The mixtures with the desired properties are then tested with the extrusion system, which includes a progressive cavity pump and an extrusion head. This is an ongoing study also including lower carbon mixes and it is expected that as the number of iterations increases, so will the qualities of the mixtures according to the given design criteria.

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14 References

1 Citations

Benz Hendrik, Nguyen Trong The, Klemt-Albert Katharina (2025-11)
Real-Time Vision-Based Defect Detection for Large-Scale on-Site Earthen Additive Manufacturing:
Annotated Dataset and Dual-Model Framework

BibTeX

@inproceedings{serg_ouel.2023.AOMDMf3CPA,
  author            = "Vasileios Sergis and Claudiane M. Ouellet-Plamondon",
  title             = "An Optimum Mix-Design Method for 3D Concrete Printing Applications",
  doi               = "10.1007/978-3-031-33187-9_61",
  year              = "2023",
  volume            = "44",
  pages             = "665--672",
  booktitle         = "Proceedings of the International RILEM Conference on Synergising Expertise Towards Sustainability and Robustness of Cement-Based Materials and Concrete Structures",
  editor            = "Agnieszka Jędrzejewska and Fragkoulis Kanavaris and Miguel Azenha and Farid Benboudjema and Dirk Schlicke",
}

Formatted Citation

IEEE

V. Sergis and C. M. Ouellet-Plamondon, “An Optimum Mix-Design Method for 3D Concrete Printing Applications”, in Proceedings of the International RILEM Conference on Synergising Expertise Towards Sustainability and Robustness of Cement-Based Materials and Concrete Structures, 2023, vol. 44, pp. 665–672. doi: 10.1007/978-3-031-33187-9_61.

CMOS

Sergis, Vasileios, and Claudiane M. Ouellet-Plamondon. “An Optimum Mix-Design Method for 3D Concrete Printing Applications”. In Proceedings of the International RILEM Conference on Synergising Expertise Towards Sustainability and Robustness of Cement-Based Materials and Concrete Structures, edited by Agnieszka Jędrzejewska, Fragkoulis Kanavaris, Miguel Azenha, Farid Benboudjema, and Dirk Schlicke, 44:665–72, 2023. https://doi.org/10.1007/978-3-031-33187-9_61.