A simulation-based optimization method for emergency evacuation induced by gas pipeline leakage risk
Bibliographic record
Abstract
Long-distance gas pipelines with large diameter and high pressure, on which a leakage induces gas diffusion or fire explosion, may result in major influence to nearby buildings and residents. Safety and rapid evacuation of potentially affected people is a top priority. To analyze the affected areas by disasters and improve evacuation efficiency of the affected areas, this study presents a simulation-based optimization method for emergency evacuation induced by gas leakage risk. First, the influence radii of different leakage accidents were calculated based on damage criteria and the evacuation radii around the accidents were determined considering the panic psychology. The number of evacuees and their spatial distribution were calculated. Secondly, an evacuation simulation model for affected communities based on the multi-agent system was established to analyze the evacuation process of residents. Finally, the optimal design method and strategies for community evacuation were proposed. Responsibility areas of organized evacuation service for community exits were determined. The results showed that the evacuation times of the two communities A and C were reduced by 10% and 24%, which indicates organized evacuation is more efficient than unorganized evacuation. The selection of community exits is more balanced. Its rationality of the proposed method was verified by the comparison of evacuation simulation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".