Evaluating Machine Learning and AHP Tools for the Pre-Qualification of Construction Contractors Based on Occupational Health and Safety Criteria
Bibliographic record
Abstract
Workers in the construction industry remain exposed to different accidents and hazardous environments. Yet, occupational health and safety (OHS) factors are the least addressed in contractors’ pre-qualification. As such, this paper presents two decision-making tools to evaluate contractors’ performance based on OHS-related criteria. Particularly, it presents the status of OHS-related orders in Alberta, Canada, and the most prominent criteria to be integrated in pre-qualification. Based on the identified criteria, a machine learning-based clustering model and an AHP model were formulated to assist in the pre-qualification process. Data related to construction OHS performance was obtained from the Workers Compensation Board between 2016 and 2020 for more than 1,500 contractors with more than 7,000 OHS-related orders issued. Orders related to (1) falls, (2) hazard assessment, (3) safeguards, and (4) entrances, walkways, stairways, and ladders were the most common ones. Both developed approaches showed the potential in supporting the contractor’s pre-qualification process.
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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 | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".