Development of Fire Safety Assessment Model for Buildings Using Analytic Hierarchy Process
Bibliographic record
Abstract
Fires pose significant risks, encompassing loss of life, destruction of property, and substantial adverse impacts on the economy. Therefore, the prioritization of fire safety in building structures must be embraced by all relevant stakeholders, including building owners, authorities, and the general public. However, traditional fire safety assessment methods can be laborious and challenging, impeding the identification of potential fire hazards and the selection of optimal fire safety measures. To this end, this research study offers an analytic hierarchy process for assessing building fire safety. Two case studies are presented to support this model’s outperformance compared with conventional assessment techniques. The proposed method yields hazard ratings of 5.3 and 4.3 along with safety ratings of 5.5 and 5.9 for the two case studies. Additionally, the proposed model yields comprehensible, well-documented, and comparable results. Therefore, it serves as a valuable decision-making tool for evaluating fire hazards and enhancing the efficiency of building structures. As a result, decision-makers can identify current and future fire protection and prevention requirements with greater ease and precision, making the decision-making process more effective.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".