Post-Occupancy Evaluation and IEQ Measurements from 64 Office Buildings: Critical Factors and Thresholds for User Satisfaction on Thermal Quality
Bibliographic record
Abstract
The indoor environmental quality (IEQ) of buildings can have a strong influence on occupants’ comfort, productivity, and health. Post-occupancy evaluation (POE) is necessary in assessing the IEQ of the built environment, and it typically relies on the subjective surveys of thermal quality, air quality, visual quality, and acoustic quality. In this research, we expanded POE to include both objective IEQ measurements and the technical attributes of building systems (TABS) that may affect indoor environment and user satisfaction. The suite of three tools, including user satisfaction survey, workstation IEQ measurements, and TABS in the National Environmental Assessment Toolkit (NEAT) has been deployed in 1601 workstations in 64 office buildings, generating a rich database for statistical evaluation of possible correlations between the physical attributes of workstations, environmental conditions, and user satisfaction. Multivariate regression and multiple correlation coefficient statistical analysis revealed the relationship between measured and perceived IEQ indices, interdependencies between IEQ indices, and other satisfaction variables of significance. The results showed that overall, 55% of occupants responded as “satisfied” or “neutral”, and 45% reported being “dissatisfied” in their thermal quality. Given the dataset, air temperature in work area, size of thermal zone, window quality, level of temperature control, and radiant temperature asymmetry with façade are the critical factors for thermal quality satisfaction in the field. As a result, the outcome of this research contributes to identifying correlations between occupant satisfaction, measured data, and technical attributes of building systems. The presented integrated IEQ assessment method can further afford robust predictions of building performance against metrics and guidelines for IEQ standards to capture revised IEQ thresholds that impact building occupants’ satisfaction.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".