Form-finding Tensegrity Models Approach with Reverse Engineering
Bibliographic record
Abstract
Background/Objectives:After recent achievements in the field of Tensegrity structure, many Tensegrity models have been investigated and evaluated. Tensegrity models have been used as symbolic or covering vast area such as fuller’s dome and other stuffs. These usages do not have sufficient attention to synthesis of architectural and structural space together.Methods/Statistical analysis: The method of this article, based on simulation and modeling of a sample structure by analyzing flow of internal forces, is adaptive methode. Restriction of exited structures to a hammock and then analysis its force flow, and consequently classify it to tensile and compressive members is the base of manner. By gathering information about Tensegrity structures and their behavior according to several definition of structural engineers and also architects, we commence combination of facts based on adaptation of existed structures with Tensegrity rules. Then, by finding a Tensegrity model and creating a replica of hammock Tensegrity, it shows the ability of structure specially in term of statistic. This outcomes can help us to develop new system of form - finding models.Findings: This system can make us able to develop modeling of Tensegrity. In the recent years, form - finding method almost base on symmetric models to expand as cover structure for vast spans. By this manner, we can design asymmetric models consist of synthesis of structural and architectural space.Application/Improvements: Form - Finding method can be developed in order to increase quality of building in term of weight of structure, flexibility, decreasing proportion of used material to its resistance and so on. In addition, we can produce asymmetric models which contain architectural space into Tensegrity structure.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".