A method for integrating occupational indoor air quality with building information modeling for scheduling construction activities
Bibliographic record
Abstract
The occupational indoor air quality during construction plays a role in determining workers’ health as construction activities frequently generate airborne pollutants. This paper presents a methodology to predict the concentration of air pollutants during construction activities using building information modeling (BIM) and schedule the activities to limit the pollutant concentration within acceptable limits. This method allows the stakeholders to determine and predict the indoor air quality at the construction site before actual construction work. The proposed method was applied to predict the concentration of PM10 during dry wall sanding in a new residence as a case study. Using BIM technology PM10 concentration was predicted during drywall sanding, which ranged from 12.8 to 29.5 mg/m3. Also the hourly PM10 concentration level and the production rate of the drywall activity was adjusted to reduce the concentration level from 20 mg/m3 to the applicable standard of 10 mg/m3. The unique contribution of this research compared to previous research on indoor air quality and BIM is the development of a new method that integrates occupational indoor air quality with building information modeling for the assessment of the occupational environment for construction workers. More specifically, this paper uses BIM to assess the occupational indoor air quality and schedule construction activities to limit the exposure of workers to prescribed guidelines.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".