Constraint-Based Design Formation - A Case Study of Wind Effects on High-Rise Building Designs
Bibliographic record
Abstract
The use of constraint-based knowledge assists designers in making well-informed decisions at different stages of digital design process. A constraint is conceived as a direct control on design formation, or as a filter which does not impose a control over a formation process. The paper aims to gain insights into the ways the designers manipulate constraints in the digital design process. It investigated whether the same type of buildings, designed by the same or different architects, presents different approaches to deal with the same constraint. Scenarios of handling a constraint using shaping and validation processes were identified before initial modelling, through modelling, and after modelling. Constraints can effectively participate in design formation by proposing a new shape or a typical shape at pre-modelling. They are refining or developing the initial design through modelling, and optimizing or enhancing the design after modelling. Furthermore, constraints play significant roles in the feasibility study of design solutions. This is done by evaluating the initial forms and selecting the fittest through modelling, in addition to testing and filtering the solution space after modelling, and approving the final design. The paper examined the impact of wind on the morphology of twelve contemporary tower designs. The findings support the opinion that constraints may not restrict the designer’s free will, inspiration, and creativity. They revealed that different scenarios of wind-driven processes had implemented at different design stages even by the same designer. The wind constraint had a substantial impact on the derivation of new morphologies. Through modelling, it had an active role in the refinement of the initial architectural and structural design. Constraint-based design was handled in iterative processes of evaluating and developing or refining the initial forms through modelling; and optimizing or enhancing, testing, and approving the final forms after modelling.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".