Project Schedule Acceleration Optimization Integrated with Energy Source–Based Assessment of Occupational Health and Safety Risks
Bibliographic record
Abstract
This research devises a risk indexing method to assess the occupational health and safety (OHS) hazards associated with major sources of energy in the construction field, providing numerical inputs to project plan and schedule optimization. Further, the problem of “minimizing project schedule at lowest safety risks” (MPSLSR) is formalized to incorporate the concept of energy sources for OHS management in project planning and scheduling optimization. Instead of following commonly applied techniques to solve multiobjective optimization problems, the proposed research takes an alternative two-step approach to minimizing project duration and risk index, based on interpretation of path float in connection with the critical path method. This results in optimized project schedules that mitigate the substantial increment of OHS-related risks due to accelerating construction progress on projects through avoiding the incurrences of unnecessary activity time crashing and associated increases in OHS-related risks. The research application is demonstrated with (1) a tunnel construction project and (2) a made-up project featuring a large, complex network model.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".