Considering diverse occupant profiles in building design decisions
Bibliographic record
Abstract
The main concern of the designer should be providing a comfortable indoor environment for occupants. Previous studies have focused on enhancing indoor conditions by addressing architectural factors related to the average comfort of users, often neglecting the fact that occupants differ in their needs and preferences in the design phase. However, designing buildings that cater to a diverse range of occupants is fundamental to successful building design. This study develops and demonstrates a workflow to integrate various occupant types at the design stage and evaluate designs based on all four domains of comfort and energy simultaneously. To achieve this objective, the study initially presents an “occupant profile sheet” derived from a survey conducted among building experts. This sheet comprises essential occupant parameters that practitioners should integrate during the design stage, aiding in the classification of occupants into distinct profiles. Next, a parametric workflow that evaluates various design options based on performance metrics for different occupant profiles is developed. Finally, it employed a case study using the proposed workflow to evaluate the impact of considering diverse occupants on the selection of optimal design parameters. The findings highlight the fact that incorporating diverse occupant types in the design process can influence architectural decisions. Overall, the research offers designers a practical guide for accommodating occupant diversity in building design, creating a more inclusive environment for a broader range of occupants.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".