Retrofit strategies to protect structures from blast loadingThis article is one of a selection of papers published in the Special Issue on Blast Engineering.
Bibliographic record
Abstract
Structural retrofits to buildings can be implemented to increase the protection level to occupants from potential terrorist bombing attacks. Retrofit strategies discussed in this paper can be categorized into three groups: (i) strengthening concepts, (ii) shielding concepts, and (iii) concepts to control hazardous debris. Strengthening concepts such as span reduction and increasing member sections are considered in this paper for three common construction systems including steel, concrete, and masonry. Shielding concepts are intended to prevent structural members from being fully loaded by blast forces and range from local area applications to entire building coverage. Examples of shielding concepts include a new section of wall that shields a vulnerable portion of the building or a new structure built over an entire building. Examples of concepts to control hazardous debris include arresting or deflecting failed cladding away from critical areas with “catch systems” or internal shield systems. This paper is intended to discuss typical building retrofit strategies for primary structural members (load bearing) and secondary structural members (nonload bearing) through strengthening, shielding, or controlling hazardous debris.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".