Probabilistic approach for identifying construction accident risk for facilities based on outdoor thermal comfort
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
When examining the environment of construction workers, it is commonly perceived they primarily work outdoors. However, in building construction, works like painting or masonry are often performed indoors. Different construction types, such as civil and building projects, expose workers to varying degrees of thermal comfort. Hence, it is crucial to assess diverse risks associated with thermal comfort in these environments. This study evaluates the relationship between facility type and thermal comfort using relative probability and uncertainty analysis, conducted in four phases. It employed k-means clustering to categorize facility types based on indoor and outdoor conditions. First, four distinct groups were identified among 44 facility types based on working conditions. Second, a consistent pattern emerged; as thermal comfort reached extreme levels (Very Cold and Warm), associated risk increased. This research contributes significantly to the field by highlighting the importance of incorporating safety management tailored to specific conditions in construction project planning. • This study evaluates relationship between facility type and thermal comfort using a relative probability analysis to identify the accident risk. • Regardless of work conditions, when thermal comfort perception approaches extreme, accident risk increased. • The results can be used to plan safety management based on weather conditions to reduce accidents and allocate resources effectively.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it