Using fuzzy ahp and fuzzy topsis approaches for assessing safety conditions at worksites in construction industry
Bibliographic record
Abstract
Providing safe workplace conditions is one of the main purposes of a safety management system (SMS) in effective construction companies.Ensuring safe workplace conditions at construction sites depends on different factors, including safety rules, management commitment, safety training, and safe behaviour.The current research aims to establish a method for identifying and evaluating the factors that impact workplace safety conditions at construction sites in Saudi Arabia.The fuzzy analytical hierarchy process (AHP) technique was used to determine and measure the qualitative factor weights affecting workplace safety to assist in the evaluation of multiple concurrent criteria.Hence, the fuzzy AHP technique was used to determine criterion weight.Alternatively, a fuzzy technique for Order Performance by Similarity to Ideal Solution (TOPSIS) model was used to evaluate the performance of companies and rank them according to their safety performance.Based on the results and findings of the presented approaches, four companies were ranked for their overall safety performance.The findings are encouraging and can be used in the construction industry to benchmark the performance of construction companies for their application of safety rules and regulations.The approach also determines the leading companies in terms of best practices and provides information for government inspectors to investigate the priorities identified for inspection.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".