Managing Safety Risks from Overlapping Construction Activities: A BIM Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Addressing safety risks in construction is an ongoing priority, and integrating safety considerations into construction scheduling is a crucial aspect of this effort. A notable challenge is the safety risk posed by concurrent tasks, which has received limited attention in prior research. This study aims to address this research gap by introducing a novel Building Information Modeling (BIM)-based model that assesses the increased hazardousness resulting from overlapping construction activities. Historically, research has predominantly focused on individual task safety, with less emphasis on the risks associated with overlapping activities. Our innovative approach introduces the concept of a ‘source–target’ match, which evaluates the degree of hazardousness escalation when activities overlap. Drawing on data from the Occupational Safety and Health Administration (OSHA) and the National Institute for Occupational Safety and Health (NIOSH) fatal accident reports, we extracted 11 hazardous and 9 susceptibility attributes to build a source–target match table. This table reveals the characteristics of activities that generate hazardous conflicts when overlapping. The key contribution of this research is the assessment, prioritization, and visualization of risk levels in a BIM environment. This framework empowers safety managers to proactively address safety risks resulting from overlapping construction activities, ultimately reducing accidents in the construction industry. By shedding light on this overlooked aspect of construction safety, our research highlights the importance of integrating safety considerations into construction scheduling and provides a practical tool for mitigating risks, enhancing workplace safety, and ultimately improving project outcomes.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it