A BIM-Based Approach for Assessing Occupational Health Risks in a Building Construction Project
Bibliographic record
Abstract
Construction work sites and the surrounding built environments are notable contributors to atmosphere dust particulate matter (PM) emissions. PM produced in construction processes contain a range of chemically hazardous substances, posing significant health risks (HR) to individuals. As such, the evaluation of occupational HR in construction has become a focal point of interest internationally. Initiated in the early 2000s, there has been a growing demand within the construction research community for the creation of a unified PM database that encapsulates a wide array of construction activities. Previous studies have endeavored to establish a PM database for various construction contexts, yet they have fallen short in thoroughly addressing the diversity of construction materials and the levels of toxic substances (TS) within the PM. This research introduced a comprehensive PM and TS dataset and conducted a case study to measure the HR associated with diverse construction processes. This was accomplished by implementing a semi-automated Building Information Modeling (BIM) version 2020-based plugin, which streamlines the assessment of occupational HR in construction projects. This system provides construction supervisors with a tool to visually assess the HR of daily operations, thereby facilitating the adoption of preemptive measures to protect the health of construction workers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".