Innovative Applications of Parametric Design and Digital Tools in Architecture: Exploring the Integration of Generative Design and BIM Technology
Bibliographic record
Abstract
Through parametric design and generative design, as well as the broader Building Information Modeling (BIM) approach, there is now a whole body of new tools being used by architects to are opening up new ways of designing buildings in innovative, efficient and sustainably responsible ways. This paper presents the emergence of parametric design and its evolution from simple structural optimisations to complex fulling the design of complete buildings and entire urban areas. The paper discusses the integration of both parametric design and generative design with Building Information Modelling (BIM). This technology pairing enables architects to research and test hundreds of design alternatives and optimise for particular variables, such as structural integrity, material efficiency and energy use. This paper showcases a number of case studies of how these technologies are applied to develop innovative and sustainable urban environments (eg, Sidewalk Toronto) and discusses the challenges associated with adopting these technologies, such as computational requirements and a lack of digital interoperability between these technologies. The paper concludes by identifying the innovation opportunities for sustainable development and the future of architectural practice that these technologies are offering.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".