Optimizing Construction Work–Rest Schedules and Worker Reassignment Utilizing Wristband Physiological Data
Bibliographic record
Abstract
Construction workers are exposed to long physically demanding work hours and require sufficient work rests to avoid strenuous fatigue, productivity loss, and adverse health effects. In the literature, limited efforts incorporated standardized work–rest periods in the construction schedule without accounting for the variation among the workers’ live physiological conditions. Rather than focusing on the worker’s fatigue data collection, this paper utilizes sample cardiovascular stress and energy exertion data from health-related studies, collected using wearable Fitbits. The paper then proposes a framework for the analysis and utilization of this physiological data to design optimum work–rest schedules and worker reassignment plans as two strategies to mitigate workers’ fatigue. The framework uses a constraint programming schedule optimization model that minimizes workers’ physiological strain through optimized work rests and worker reassignments. Using a hypothetical schedule with the workers’ fatigue data, the model proposed two 20-min breaks for one worker and two 5-min breaks for other workers and reassigned eight workers to different tasks. The framework helps project managers to efficiently improve the health and safety of workers and improve productivity, leading to a more inclusive work environment.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".