A Simplified Methodology for Rapidly Analyzing the Effect of Multi-Hazard Scenario on Atmospheric Storage Tanks
Bibliographic record
Abstract
Abstract Natural hazard events that trigger technical emergencies (Natech events), as a typical type of multi-hazard, have become a matter of growing concern. In particular, the occurrence of Natech events in industrial areas triggered a number of severe accidents. The present research aims at introducing a sound but simplified methodology to quickly and flexibly assess the vulnerability of atmospheric storage tanks to multiple natural disasters in Natech events. This method consists of 8 steps, relying on the simplified physical models of tank damage caused by natural disasters. The models of wind overturning tank and tank buckling caused by hail are proposed. In addition, the assessment process of tank vulnerability is demonstrated from two aspects: deterministic analysis and probabilistic analysis. The uncertain parameter set (UPS) proposed in the method and the Monte Carlo simulation method can help to purposefully analyze the impact of various parameters and this method is also a general method, which is also applicable to Natech events including other natural disasters or other types of storage tanks.
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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 | 0.061 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".