A novel fault tree analysis approach to investigate uncommon accidents in quarries: a case study
Bibliographic record
Abstract
Quarrying companies are increasingly involved in developing effective Occupational Health and Safety Management Systems (OHSMS) to protect both their own staff and all those collaborating with their organization.The adoption of schemes such as BS OHSAS 18001:2007 helps the company to comply with legal requirements and serves the personnel both by raising awareness of the potential adverse consequences to which they are exposed (i.e., the risk of accident and potential occupational illness), and by limiting and managing the risk of occupational hazards.Since the quarry environment presents many hazardous factors, various approaches to monitor and control their effects are required; among them the choice of OHS risk assessment methods and the approach to accident investigation being the most crucial.This paper refers to a case study of an unusual accident which occurred in an Italian quarry recently.The analysis aims to identify and describe the true course of events, as well as to analyse the direct causes and contributing factors of the accident, the main objective being to identify best practices in risk reducing measures in order to prevent similar accidents occurring in the future.Initially a deductive approach is taken using the Fault Tree Analysis (FTA) method.This is then improved by integrating a Management Oversight and Risk Tree (MORT) technique and Multiple Cause Systems Oriented Incident Investigation (MCSOII).The proposed analysis is finally utilized to design and define procedures to manage these particular activities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".