Innovations to Improve 3D Concrete Printing of Portland Cement-Steel Slag Blended Mortars
Bibliographic record
Abstract
This paper identifies typical issues and their remedies during lab-scale 3D printing of Portland cement-steel slag blended mortars. This study used a printer with an accelerator in its feeding system immediately before the extrusion stage. Accelerator dosage can be regulated for such a printer even during printing. However, a higher or lower than optimum dosage may lead to excessive flow or dry surface with a potential risk of cracking in the individual layers. Through the current study, two hollow cylindrical geometry and one hollow square geometry were printed. The print quality was evaluated by investigating various variables, such as considering two superplasticizers with different open times and using constant and variable accelerator dosages during printing. The compatibility of superplasticizers was found to affect the open time and, hence, the print quality with layer shortening and breaking. Changes in accelerator dosage during printing to compensate for the changing rheology were similarly notable, especially in terms of inconsistencies in printed layers. However, the use of a compatible superplasticizer was determined to mitigate both issues. Additionally, shrinkage-reducing admixture was recommended for mortars to avoid cracking due to early-age drying shrinkage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".