Coupling of advanced techniques for dynamic risk management
Bibliographic record
Abstract
Identification and assessment of hazards and risks in the activities of the process industry are of paramount importance for the prevention of major accidents. Although several techniques of HAZard Identification (HAZID) and quantified risk analysis have often been proved effective in the industry, they generally lack the dynamic dimension of risk management. In other words, they lack the ability to learn from new risk notions, experience and early warnings. When carrying out HAZID and risk assessment, there is the need to know how to deal with atypical accident scenarios as soon as their emergence is demonstrated. The related risk needs to be addressed in an ever-changing environment. In fact, what is not identified or assessed cannot be prevented or mitigated and latent risk is more dangerous than recognized one due to the relative lack of preparedness. This study proposes a dynamic approach to risk by coupling an advanced technique for hazard identification to an innovative method for risk assessment: the Dynamic procedure for atypical scenarios identification (DyPASI) and the Dynamic risk assessment (DRA) method. DyPASI was developed within the EC project iNTeg-Risk. This technique aims to complete and update HAZID. Atypical accident scenarios, which by definition are deviating from normal expectations of unwanted events or worst case reference scenarios, are identified through a systematic screening of related emerging risk notions. The DRA method aims to estimate the updated expected frequency of accident scenarios by means of Bayesian inference. Real time abnormal situations or incident data are used as new information to update the failure probabilities of the system safety barriers, which necessarily affect the overall scenario frequencies and the related risk profile. The BP Texas City refinery accident, that occurred on 23 March 2005, was considered as a case study. The results obtained from the application of the dynamic risk approach show that the accident should have been expected and its occurrence probability could have been reduced through this approach. The results highlight the need of safety culture and decision-making processes capable of dealing dynamically with emerging and increasing risk issues.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".