Fire Detection Systems in Road Tunnels - Lessons Learnt From an International Research Project
Bibliographic record
Abstract
Today's increase in road traffic, changes in vehicle mix, and new rolling stock have resulted in an increase in the incidence of fatal fires in tunnels. In Europe, recent catastrophic tunnel fires have resulted in loss of life and severe property damage. Reliable and early fire detection in tunnels insures an early warning of a fire incident, allowing for timely activation of emergency systems. As such, detection can make the difference between a manageable fire and one that gets out-of-control [1, 2]. The Fire Protection Research Foundation recently completed an international research project, with the support of private and public-sector organizations to evaluate the performance of various types of detection [3]. The project studied the detection performance of nine fire detection systems representing five types of currently available fire detection technologies (Table 1), including their response times to a fire and ability to locate and monitor a fire in a tunnel, with both laboratory and field fire tests combined with computer modeling studies. In addition, the project also studied the reliability of the detection systems in harsh tunnel environments, such as their nuisance alarm immunity and requirements for maintenance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".