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 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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 teacher head, 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".