Review of the fire risk, hazard, and thermomechanical response of bridges in fire
Bibliographic record
Abstract
Resilient design requires information about a structure’s response to a variety of exposures such that systems can be implemented to prevent unacceptable losses. For the case of critical infrastructure like bridges, losses associated with structural damage and traffic closures from fire events can be substantial. Despite this, there are no specific code requirements for bridge fire safety in different national jurisdictions, particularly in North America and Europe, and only minimal guidance available for establishing the fire resistance requirements of bridges. Research into the fire safety of bridges is ongoing but knowledge gaps persist that limit practitioners’ ability to conduct performance-based fire designs using the latest state of existing research. This paper provides a first-stage state of the art review of bridge fire research conducted to date in effort to summarize key findings and make available the most relevant information for researcher and practitioner use. The key research themes considered as subdivisions are fire hazard and risk assessment, bridge fire scenario modelling, and the structural response of steel and composite steel-concrete, cable-supported, concrete, and fiber reinforced polymer bridges to fire. The authors conclude the study with identified knowledge gaps and priority research areas.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".