Probabilistic modelling of safety and damage blast risks for window glazingThis paper is one of a selection of papers in the Special Issue on Blast Engineering.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There are many computational techniques to model the consequences to built infrastructure when subject to explosive blast loads; however, the majority of these do not account for the uncertainties associated with system response or blast loading. This paper describes a new computational model, called “Blast-RF” (Blast Risks for Facades), that incorporates existing (deterministic) blast-response models within an environment that considers threat and (or) vulnerability uncertainties and variability using probability and structural reliability theory. The structural reliability analysis uses stress limit states and the UK Glazing Hazard Guide's rating criteria to calculate probabilities of glazing damage and occupant safety hazards conditional on a given blast scenario. This allows the prediction of likelihood and extent of damage and (or) casualties, useful information for risk mitigation considerations, emergency service's contingency and response planning, collateral damage estimation, weaponeering, and post-blast forensic analysis.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it