Integrating Universal Design Standards and Building Information Modeling at the Conceptual Design Stage of Buildings
Bibliographic record
Abstract
A projection of the Canadian population shows that in 2024 one in five Canadians will be over 65 years old. This shift forces designers to consider the entire lifetime of occupants during the design of new buildings. Universal Design (UD), which is a design that accommodates all people to the greatest extent possible and aging in place design that is deeply rooted in the principles of UD, aim to house people irrespective of their age, ability, and chronic health conditions. Building Information Modeling (BIM) significantly helps advance the development of the Architecture, Engineering, and Construction (AEC) industry in a more collaborative and automated way. Integrating BIM and UD allows designers to incorporate UD standards easily and efficiently at the conceptual design stage of buildings by using the functionalities and capabilities of BIM tools. Therefore, this study presents the development of an automated computer model to facilitate the adoption of UD standards and processes. The novelty highlighted in this model resides in the creation of an automated method that employs a newly created plug-in and databases to assist designers to incorporate UD standards at the conceptual stage in a timely and cost-effective manner. Furthermore, the study introduces the methodology consisting of collecting, categorizing, and storing data from various universal design and accessible design guidelines in the developed databases and developing new plug-ins in BIM tool to link the developed databases in order to automate the process of retrieving necessary information and components to help designers and owners select optimal design alternatives based on their predefined criteria.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".