Joint Exploration and Analysis of High-Dimensional Design–Occupancy Templates
Bibliographic record
Abstract
Crowd simulations provide a practical approach to evaluate building design alternatives with respect to human-centric criteria, such as evacuation times and flow in case of emergency scenarios. Coupled with Building Information Modeling (BIM) tools, they support architects’ iterative exploration of design alternatives. However, methods based on manually configuring a design and a corresponding simulation are not practical for exploring the potentially very large number of design solutions that satisfy human-centric design goals and requirements. Often, for practical reasons, designers may consider standard crowd configurations which do not capture the behavior of diverse occupants that may exhibit different locomotion abilities, movement patterns, and social behaviors. We posit that a joint exploration of high-dimensional building design and occupancy features is necessary to more accurately capture the mutual relations between buildings and the behavior of their occupants. To test this hypothesis, we conducted a series of experiments to automatically explore joint high dimensional design–occupancy patterns using an unsupervised pattern recognition technique (i.e. K-MEANS). We demonstrate that joint design–occupancy explorations provide more accurate results compared with sequential exploration processes that consider default design or crowd features, despite the longer computational times to simulate a large number of solutions. The findings of this case study have practical applications to the design of next-generation design exploration tools that support human-centric analyses in architectural design.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".