A cost‐effective building fire smoke spread prediction approach for risk mitigation based on data assimilation using Ensemble Kalman Filter
Bibliographic record
Abstract
Summary Prediction of building fire smoke spread behaviors plays an important role in the evacuation in an emergency to mitigate fire risk. Based on data assimilation, a cost‐effective approach was proposed to predict fire smoke spread behaviors under unknown mixed disturbances by combining a zone model (CFAST) and Ensemble Kalman Filter (EnKF). CFAST is used to predict fire smoke spread behaviors as a deterministic fire model. Sensor data of smoke temperature were assimilated into CFAST simulation by leveraging EnKF to estimate unknown heat release rate (HRR) and thus improving the prediction accuracy. The performance of the proposed approach was tested via a series of Observing Systems Simulation Experiments. Two series of cases were conducted for unknown single HRR change disturbance and unknown single‐window breakage disturbance, one series of cases was conducted for unknown mixed HRR change and window breakage disturbances. Result comparisons have been presented in figures to assess the approach performance qualitatively, and root mean square errors (RMSEs) have been calculated to assess the approach performance quantitatively. The RMSEs are within the range of 1.78–29.88 K. The results showed that it was a viable solution to set a larger perturbation range for unknown disturbances. The proposed approach could provide more accurate and reliable predictive information about fire smoke spread behaviors for risk mitigation, as well as other safety‐related applications.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; 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".