A resilience evaluation method for multi‐hazard domino‐effect accidents in chemical industry parks considering safety barriers
Bibliographic record
Abstract
Abstract The concentration of chemical enterprises in chemical industry parks (CIPs) has led to the accumulation of hazardous chemical risks, frequent multi‐hazard coupling accidents, and escalation of domino effects. Existing evaluation methods struggle to characterize the interrelations between hazards, adaptability, and recovery characteristics. This work proposes a resilience‐assessment method for multi‐hazard coupling domino‐effects accidents in CIPs, considering safety barriers to fill these gaps. First, multi‐hazard coupling scenarios are identified by integrating the temporal clustering and spatial aggregation features of hazards. Second, the hazard disruption‐system feedback response mechanism is analyzed to establish a quantitative resilience model for CIPs. Third, the probabilities of multi‐hazard interactions and domino‐effect escalation are quantified to evaluate the influence of safety barriers on accident occurrence probabilities. Finally, case simulations are conducted to compare the impacts of different safety‐barrier configurations on resilience, providing recommendations for optimizing safety barriers in CIPs. Results indicate that the effectiveness of safety barriers significantly influences the strength of system adaptability and recovery capabilities in multi‐hazard coupling domino‐effect scenarios. Their performance directly affects the trough depth and recovery slope of the system‐performance curve.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".