A study of digital and physical workflows used for the creation of fabric-formed ice shells with bending active frames
Bibliographic record
Abstract
Cold climate regions with sustained temperatures between −10°C and −20°C offer a unique opportunity to produce temporary rigid ice spatial structures. An advantage offered by creating these unusual structures includes their ability to test the structural performance of spatial shells made from this and other analogous liquid-to-solid materials (e.g. concrete, GFRC, fiberglass, etc.) at a building scale. Additionally, because of the minimal cost of the material and temporary life of these structures, they offer a unique opportunity to explore and improve the design and construction methods used to erect shell structures in an efficient and low impact way. This paper focuses on the creation of fabric-formed ice shells utilizing bending active frames as a form-finding system. In particular, the paper will analyze the design process workflows used in three case studies of building-scale ice shell projects created by the authors and highlight the tools and methodologies developed to address the particular goals of each project. Responding to the lessons learned from these projects, a final project describing current research will be presented. In this work there is an effort to synthesize the lessons from the three previous projects and produce a congruent, iterative, and effective design and construction workflow to produce fabric-formed ice shells using bending active gridshells. An emphasis in this study focuses on the informational and methodological transfer between digital and physical tools and how these unique tools and capacities can create a synergistic design and construction language that leverages the limitations of one with the strengths of the other.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".