Assessment of civilian structures for military applications
Bibliographic record
Abstract
The increasing tendency to use urban civilian buildings for military purposes prompts the need for the assessment of their blast resistance. Many of these buildings are made of reinforced concrete (RC). Popular tools available for the assessment of existing RC structures in practice include guidelines and design standards, technical manuals and specialised software. These tools include certain assumptions based on scarcely available test data, as historically they were collected for military purposes. Efforts to transfer this knowledge from military to civilian applications are relatively recent and need be corroborated by further testing and numerical analysis. The objective of this paper is to present the results of field tests on full-scale RC members to check the validity of a number of assumptions routinely made in current numerical/analytical models. The data captured during the tests, including reflected pressure and member displacements, are compared with results of empirical and numerical models, in order to gauge the robustness and accuracy of the assumptions underpinning these models. Finally, recommendations are made for an expedient assessment of existing buildings based on simple methodologies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".