Bayesian network approach to human reliability analysis (HRA) at offshore operations
Bibliographic record
Abstract
This thesis presents a quantitative approach to human reliability analysis (HRA) in offshore emergency conditions. Most of the traditional HRA methods use expert judgment techniques as human performance data for emergency situations are not readily available. Expert judgment suffers from uncertainty, incompleteness and when collected from multiple experts, may have conflicting views. This thesis investigates these limitations and presents a proper aggregation method to combine multiple expert judgments using Fuzzy Theory to handle the uncertainty and Evidence Theory to handle the incompleteness and conflict. Furthermore, the traditional approaches of HRA suffer from the unrealistic assumption of independence among different performance shaping factors (PSFs) and associated actions. This thesis addresses this issue using the Bayesian network (BN) approach which can represent the interdependencies among different PSFs and associated actions in a direct and structured way. The integration of Fuzzy Theory and Evidence Theory to the BN approach gives an HRA model that can better estimate the success or failure likelihood of personnel in offshore emergency conditions. Incorporation of environmental factors makes the model applicable for offshore emergencies occurring in harsh environments. Finally the thesis presents a new methodology to collect human performance data using a virtual environment. Using the collected data, a simplified BN model of offshore emergency evacuations is tested and verified.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".