Transformable structures: Materialising design for change
Bibliographic record
Abstract
Living in an age of rapid changes, designers are challenged to create solutions that remain sustainable in a continuously evolving environment.Since most of our earth's resources are finite, these solutions should incorporate efficient material use and reuse.Buildings and structures are always in transition.Facilitating these transformations is vital to the sustainable development of our built environment.With our group we study, develop and assess transformable structures on different scales, in different contexts and for various time-spans and purposes.This paper presents our work on transformable structures, based on four case studies: a kinetic curved-line folding component, a temporary and rapidly assembled structure, a dynamic wall assembly and a BIM tool for material flow assessment of adaptable buildings.Although varying in scale or purpose, these cases demonstrate the same key principles of transformability.Reducing the complexity of the connections and structural system facilitates an easy and rapid assembly, but also allows users and locals to participate in the assembly, maintenance, reconfiguration and deconstruction of the structure.Apart from benefits during the assembly and adaptability, it is important to assess transformable structures and building solutions on their material and cost effectiveness.With BIM tools it is possible to incorporate this assessment already in the conceptual design phases of a project, as illustrated in the fourth case.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| 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".