Intuitive interactive form finding of optimised fabric-cast concrete
Bibliographic record
Abstract
Producing organic shapes in concrete has been a challenging problem since complex freeform buildings became a major trend in contemporary architecture. Many different techniques for casting doubly-curved shapes have been proposed. Most of them produce elements which exactly match a preconceived design, but at a high cost in manufacture. Fabric formwork techniques (such as those pioneered at the Centre of Architectural Structures and Technology at the University of Manitoba (CAST)) are relatively economical, but require a form-finding approach which takes into account the physics of casting, as well as structural and functional requirements of the finished element. The research presented here involves a specialised methodology for the design and manufacture of optimised concrete elements cast in fabric formwork. Using a novel software tool, our approach lies in between the largely intuitive methods reported by CAST and the precise but expensive computer-controlled manufacturing methods normally used in practice. Combining topological optimisation with computational form-finding, the developed software guides the designer towards a shape that is economical in both material and manufacturability. By combining knowledge of computational structural analysis, optimisation algorithms, fabric simulation and the practical casting techniques of fabric formwork; the gap between structurally optimised forms, and those developed intuitively by fabric casting, can be bridged. This is demonstrated through a case study involving the computational design of a centrally supported slab, supplemented with design studies realised using plaster scale models.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".