Fuzzy inference system-Latin hypercube simulation: An integrated hybrid model for OHS risks management
Bibliographic record
Abstract
Risk management in construction industry in several cases is not only incomplete regarding the unification of Occupational Health and Safety (OHS) hazards, but it is also incomplete in not having a systematic and innovative method to assess the impacts of these risks on the objectives of a project.An integrated hybrid Fuzzy Inference System-Latin Hypercube Simulation for the evaluation of OHS risks in construction projects is presented in this paper.Prioritization of safety risks systematically without human interference with fuzzy inference system gives the appropriate response to the identified risks.An advanced Monte Carlo simulation is also used for the evaluation of quantitative objectives of a project.This approach allows us to get away from discrimination and simulate the risks with high impacts but with low probabilities.In order to measure the relationship between the occurrences of each of the risks impacts on project objectives, the sensitivity analysis based on Pearson correlation coefficient is used to determine the usefulness of the proposed integrated hybrid method.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".