Integrating Indoor Environmental Quality and Decision-Making at Early Building Design
Bibliographic record
Abstract
People spend about 90% of their time indoors.This extended exposure to indoor conditions affects occupants' well-being and productivity.Early design decisions have a profound impact on indoor environmental quality (IEQ), yet existing IEQ-related assessments normally wait until the post-occupancy evaluation when few opportunities for design improvement exist.Instead, IEQ should be assessed during a design phase when design decisions are less costly and more influential.The presented research outlines a building design framework that integrates occupant comfort and diversity to holistically evaluate IEQ and enhance building performance.To achieve these objectives, the proposed thesis is divided into three main parts.First, this study aims to introduce an efficient simulation-based framework involving parametric modelling to simultaneously quantify the impact of design decisions on all four domains of IEQ, namely, thermal comfort, visual comfort, acoustic comfort, and air quality.The results indicated the importance of considering all comfort domains together, as one design choice might improve one IEQ domain at the cost of others.Then, it provides guidance on how to weigh each domain and corresponding metrics, particularly during I embarked on the journey of pursuing my PhD alongside the journey of motherhood, facing numerous new challenges and much confusion.It was far from easy, but it became possible.I am certain that I couldn't have achieved this without the support of my supervisor and my family.I want to express my appreciation to my supervisor, Professor William O'Brien, for his unwavering support and mentorship.You gave me the freedom to pursue my goals and shape the direction of my work while helping to keep me on the right path.A warm thanks to my friends
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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 | 0.006 |
| 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 | 0.003 | 0.002 |
| 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".