Using the Pivot Pair-Wise Relative Criteria Importance Assessment (PIPRECIA) Method to Determine the Relative Weight of the Factors Affecting Construction Site Safety Performance
Bibliographic record
Abstract
In the construction industry, safety issues are considered major concerns.Despite recent attempts to improve safety in the construction sector, this sector is considered dangerous and unsafe.Construction safety management in Iraq is plagued by a high incidence of construction accidents, resulting in a higher number of injuries and fatalities.Creating a safety program is one strategy to alleviate this problem by making safety an intrinsic part of construction projects.Defining which factors are the most significant and have the greatest effect on safety performance is crucial to building a complete safety program.It ensures that construction companies are not wasting money on inadequate safety programs.As a result, this article aims to identify the critical safety factors that influence safety performance in Iraqi construction projects and assign a relative weight to them based on their importance.First, relevant literature was reviewed to identify the safety performance factors.Second, the Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA), a recently proposed technique for calculating criteria weights, was employed to determine the relative weight of factors.In this paper, a list of 21 sub-factors classified into 8 categories of main factors was identified.Finally, the findings of PIPRECIA show that the "Management Practices" factor has the top rank with a weight of 0.4221 among the other main safety factors.The results also showed that among the 21 sub-factors, the three with the highest weights, 0.0686, 0.0665, and 0.0619, belonged to "Personal protective equipment (PPE)", "First aid and medical care" and "Housekeeping program", which were under the "Management Practices" factor.Further, the "Regular safety inspections" and "Contractor safety rewards and punishment programs" sub-factors were identified as the least important sub-factors with weights of 0.0350 and 0.0333, respectively.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".