Rapid Screening of Buildings for Blast Risk Assessment
Bibliographic record
Abstract
The risk of building against blast effects can be determined by means of a threat risk assessment (TRA). A TRA involves establishing the potential threat, determining the vulnerability of the building against the established threat, evaluating the consequence caused by the realization of the threat and finally assessing the risk of the buildings against the blast threat. A full TRA can be a time-consuming and costly undertaking, especially for owners of a large inventor of buildings. A rapid screening methodology has been developed for conducting a preliminary assessment of buildings against blast effects. The methodology accounts for the threat, the vulnerability of the building, the consequence of the vent and the risk, which is the product of the threat and the consequence. Rapid screening of one building should take no more than 2 days to complete. By ranking buildings according to their risk values, prioritization of buildings to undergo a full TRA can be established for determining if and what retrofits are required to mitigate the risk. The rapid screening methodology can also be used in the concept design phase of a new construction in terms of evaluating and comparing the blast risks of various concept designs. The rapid screening methodology is currently undergoing validation testing. It is being applied to ten buildings and the resulting rankings will be compared with the results of other risk assessment methods. At this time the preliminary results appear to be promising. The paper presents the rapid screening methodology and its application to two federal buildings in Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".