Design Tools for Foldable Structures with Application of Fuzzy Logic
Bibliographic record
Abstract
Rigidly foldable shells offer tremendous potential for developing kinetic architectural structures. However, the added element of motion poses new design challenges. Initially, sketchy shell geometry is constructed to reflect the intended form. Further steps involve assuring an error free folding within a range that satisfies desired functional requirements. The kinematics of a parallel topology of the shell's geometry is difficult to express algorithmically what prevents from developing of automated adjustment tools based on computational methods. The geometry can be adjusted manually based on intuitive observations; however the process is tedious, time consuming and unpredictable. This paper develops automated adjustment tools based on the intuitive approach of a human designer. The study applies the fuzzy logic formalism as a computational interface between human approach and structured adjustments to the geometry. The advantages of fuzzy logic stem from its natural ability to represent human knowledge and effectiveness in reconciling ambiguities, uncertainties and redundancies that the intuitive human approach brings along. The development steps of fuzzy logic based algorithm are presented. Performed evaluation tests and the results are discussed.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".