Review of post-earthquake fire hazard to building structures
Bibliographic record
Abstract
Fire following an earthquake is an important factor causing damage to buildings and life-line structures. Therefore, besides satisfying structural design requirements for normal loads, such as dead and live loads including the seismic hazard, buildings should also be designed to withstand the fire following earthquakes for a certain minimum duration as required for a desired level of performance. This period of time will allow occupants to evacuate the building safely and the emergency crews to cope with the fire. Also, it is essential to reduce the post-earthquake fire (PEF) ignitions and minimize the damage to active fire protection systems as much as possible to prevent the spread of fire. This paper presents a state-of-the-art review on the PEF hazard and discusses the causes, mitigation measures, and performance of building structures under this hazard. Mitigation measures that could be developed based on the experience from the structural engineering field are identified. Both local and global approaches that should be taken to mitigate the PEF hazard, including structural and nonstructural design, various urban planning aspects, and their interactive combinations, are discussed. Based on the review, it is concluded that that there is a strong need for the development of guidelines for structural fire safety design for PEF scenarios. In addition, appropriate analysis and numerical simulation techniques for the evaluation of the structural performance under earthquake-induced fire conditions need to be developed. It is also necessary to conduct experimental studies to validate such numerical models and refine them.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".