Fresh State Requirements for 3D Printable Mortar Mix
Bibliographic record
Abstract
Significant innovations have been achieved in the building sector with the use of 3D printing technology, which enables the printing of complex structures with less time, labor, waste, and costs.The key quest in this technology is to have a printable material that must achieve several requirements in order to be printable, such as achieving good flowability and then structural stability after extrusion, otherwise, this can cause damage to the printer and potentially lead to greater maintenance expenses.Therefore, this research uses laboratory tests to check the fresh state properties that the mix must achieve in order to be printable.Slump, slump flow, manual device gun, slug test, uniaxial unconfined compression test, and vicat apparatus have been used to characterize the formulated mix at fresh state and check its printability.These tests are simple to use, require less manual labor, materials, time to prepare, and provide fast results with little post-processing needs.A good prediction of the material behaviour has been obtained using these tests, checking that the material achieves the general fresh state properties flowability, extrudability, buildability, and open time.Then, checking the elevation of yield stress and green strength in function of time in order to ensure that the material can be buildable and sustain its weight without any failure.Finally, these tests have given a good sign for having an appropriate printability by successfully printing multiple shapes without any failure.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".