Integrated fuzzy fault tree-Bayesian network modeling for rollover risk assessment of LNG road tankers
Bibliographic record
Abstract
Liquefied natural gas (LNG) road tanker rollover accidents, though infrequent, often lead to catastrophic consequences. Quantitative risk assessment is significantly challenged by the scarcity of probabilistic data specific to these complex, low-frequency events. To address this data limitation and enhance assessment accuracy, this study develops an integrated fuzzy fault tree-Bayesian network (FFT-BN) methodology. Fuzzy set theory is applied, leveraging multi-source general traffic accident statistics and expert judgment, to quantify the occurrence probabilities of basic causal factors under uncertainty. A Bayesian network is then constructed from the fault tree structure to enable comprehensive probabilistic inference. Critical risk factors were rigorously identified using multiple importance measures (ROV, BIM, RRW). The analysis consistently pinpointed poor road alignment and the absence of critical traffic facilities as the two paramount contributors. Crucially, vehicle speed management emerged as the central mitigation mechanism linking these factors; controlling speed effectively counters the destabilizing effects of poor alignment and compensates for the lack of timely hazard perception. The results demonstrate that implementing targeted speed control measures on identified high-risk road sections is essential for reducing the probability of LNG tanker rollovers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".