A simulation-based approach for evaluating indoor environmental quality at the early design stage
Bibliographic record
Abstract
People spend about 90% of their time indoors. This extended exposure to indoor conditions affects well-being and performance. Design decisions have a profound impact on IEQ, yet existing IEQ-related assessments normally wait until the post-occupancy evaluation when few opportunities for design improvement exist. This study introduces an efficient simulation-based framework involving parametric modeling to simultaneously quantify the impact of design decisions on all domains of IEQ, namely, thermal comfort, visual comfort, acoustic comfort, and air quality. To achieve this goal, first a set of metrics and corresponding evaluation methods to quantify the four IEQ domains are developed. Then, a number of design parameters that impact multiple IEQ criteria in the design stage including office geometry, facade design, and material properties are investigated. Next, the aggregated score for each domain is presented to measure the room’s IEQ performance. A case study is considered to demonstrate the proposed workflow. The results indicate the importance of considering all comfort domains together, as one design choice might improve one IEQ domain at the cost of others. The proposed workflow is an efficient and effective method for architects and other building stakeholders to compare design scenarios with regards to IEQ performance at preliminary design stages.
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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 | 0.002 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".