Using real-time sensing data for predicting future state of building fires
Bibliographic record
Abstract
The development of sensor technologies in recent years makes it possible to acquire real-time states of building environment and its systems with a major trend towards big data and wireless data transmission. How to use these vast real-time data sets to achieve a safer, more comfortable and energy efficient building becomes a major challenge for building engineering. This paper investigates one of the possibilities of using the real-time data for the prediction of future fire development states. Beyond simply reflecting the real-time states of a system, the sensing data will be able to forecast a highly dynamic problem of building fire growth and smoke dispersion inside building environment. This paper presents the forecasting method based an ensemble Kalman filter (EnKF) to predict building fire smoke temperature and smoke layer height at the real time. Detailed formulations of the zonal fire smoke models and the EnKF model are presented. The proposed real-time forecasting method is demonstrated and validated by a 1:5 scaled compartment fire experiment. The results indicate that real-time forecasting of building fires is achievable while the accuracy is noticeable which can be applied to assist emergency evacuation and firefighting.
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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".