Blast Analysis, Design and Research: A Canadian Perspective
Bibliographic record
Abstract
In order to protect structures against accidental or deliberate blast loads, special expertise and knowledge are necessary to ensure adequate performance of the structural systems. Although the structure may require an extensive repair following a blast event, the main goal of the protective design is to avoid structural progressive collapse and minimize fragments. Blast loading is very different from other forms of dynamic loading generally analyzed by structural engineers. Peak pressures are several orders of magnitude higher than those associated with other typical dynamic loads, and blast load durations are usually much shorter than the fundamental period of the structure. This paper will focus on the blast loading phenomena, analysis procedures and design implications as specified by the two recently developed North America’s codes, ASCE SEI59-11 (2011) and CAN CSA S850-12 (2012). In addition, the results of an ongoing multi-year large-scale experimental and analytical program at McMaster University, Canada will be highlighted.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".