Measuring and Predicting Fatigue in Construction: Empirical Field Study
Bibliographic record
Abstract
The increasing commitment to safety over the last two decades has contributed to a 67% decline in recordable incident rates. The rate of fatalities, however, has recently increased. Human factors, like fatigue, strongly relate to fatalities. The prediction of fatigue would allow for an early intervention, thus mitigating safety risk. The literature suggests several potential predictors of fatigue onset; however, each of these was mainly studied in isolation, in laboratory settings, and their predictive validity in the construction industry remains unknown. The authors hypothesized that a set of measurable factors can predict construction worker fatigue. A field study of 252 US construction workers was conducted in which potential predictors and fatigue levels were assessed, and the first fatigue predictive models for construction workers were created. The models presented low to medium predictivity, demonstrating that laboratory research and results obtained from other occupations do not directly apply to the construction industry. Furthermore, fatigue predictive models showed to differ among trades. These models will serve the industry to better manage fatigue; however, further research in this area is needed.
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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 | 0.011 | 0.024 |
| 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 | 0.002 |
| 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 | 0.000 |
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".