Crafting plaster through continuous mobile robotic fabrication on-site
Bibliographic record
Abstract
Abstract Industrialization of architectural components and technological advances have had a significant impact on how we design and build. These developments, resulting in mass-produced and panelized architectural components, have rationalized building construction. However, they often do not reveal the true potential of the inherent qualities of malleable materials. This research investigates the bespoke design potentials of combining a cementitious plaster, with a robotic spraying and forming process, and proposes an adaptive thin-layer additive manufacturing method for plasterwork. Research goals address an on-site construction system that is capable of performing continuous robotic plaster spraying on building elements. To support the understanding of the complex-to-simulate material behavior in this process, systematic studies and physical testing are proposed to be conducted to collect empirical knowledge and data. The goal is to explore bespoke surface qualities, with minimal waste, moving away from the modular and standardized form of the material. The paper presents the preliminary results and findings of the method that aims addressing the challenge of an adaptive construction system capable of performing continuous fabrication, for which mobile robots are proposed to be deployed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".