Dynamic Risk Analysis Using Imprecise and Incomplete Information
Bibliographic record
Abstract
Abstract Accident modeling is a vital step, which helps in designing preventive measures to avoid future accidents, and thus, to enhance process safety. Bayesian networks (BN) are widely used in accident modeling due to its capability to represent accident scenarios from their causes to likely consequences. However, to assess likelihood of an accident using the BN, it requires exact basic event probabilities, which are often obtained from expert opinions. Such subjective opinions are often inconsistent and sometimes conflicting and/or incomplete. In this work, evidence theory has been coupled with BN to address inconsistency, conflict and incompleteness in the expert opinions. It combines the acquired knowledge from various subjective sources, thereby rendering accuracy in probability estimation. Another source of uncertainty in BN is model uncertainty. To represent multiple interactions of a cause–effect relationship Noisy-OR and leaky Noisy-AND gates are explored in the study. Conventional logic gates, i.e., OR/AND gates can only provide a linear interaction of cause–effect relationship hence introduces uncertainty in the assessment. The proposed methodology provides an impression how dynamic risk assessment could be conducted when the sufficient information about a process system is unavailable. To illustrate the execution of a proposed methodology, a tank equipped with a basic process control system has been used as an example. A real-life case study has also been used to validate the proposed model and compare its results with those using a deterministic approach.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 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".