A study of digital and physical workflows used for the creation of fabric-formed ice shells with bending active frames
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it