Matter-Robotic Calibration for Bioshotcrete
Bibliographic record
Abstract
Construction techniques associated with traditional raw earth archi- tecture are characterised by laborious manual tasks in which each clay mix is deposited in layers over a light formwork, such as with the wattle and daub technique. More sustainable solutions also exist for the use of concrete, including shotcrete or sprayed concrete over light form- work composed of fabrics, inflatables or metal meshes. This research explores robotic techniques for the digital fabrication of monolithic earthen shells, with the objective of reformulating the use of clay as a sustainable material to reduce laborious tasks, minimize the use of formwork, and to implement robotic fabrication processes. This unique technique is called “bioshotcrete” and is characterised by an innova- tive fabrication process of sequential robotic spraying deposition of different natural raw clay mixes over a temporary light formwork. Two case studies are described and analysed featuring two distinctive tech- niques: clay mixes sprayed with a robotic arm and with a drone. Details are highlighted, and key considerations are identified, in terms of subtle adjustments for the material formulation and application sequences, robotic tooling strategies, and customised robotic actions. This series of experiments was formulated as an ongoing experiment to address chal- lenges related to limitations of reaching distances and lightness of ma- chines to bring on site, and to explore newfound possibilities for aerial deposition techniques using drones. Variations related to Tool/Matter performance (spray velocity and surface adhesion) were explored at each clay mixture iteration. Additional improvements were identified by recent physical tests, such as using the drafts created by the drone he- lixes to help the drying process at each layer, and additional conclusions establish how this technique is not only shaping new design and digital fabrication processes but envisioning possible future applications and offering new scenarios for sustainable large-scale earthen envelopes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".