Blast Resilient Design of Infrastructure Subjected to Ground Threats
Bibliographic record
Abstract
The growing number of terrorist attacks in the past decade has focused the public’s attention on the severity of such a man–made hazard. The rising threat of improvised explosive devices — one of the most successful attack strategies — has significantly increased the number of threats on the ground, in the form of suicide–bombs, vehicle–bombs, etc., thereby requiring the development of more effective blast risk mitigation measures. However, the modern proliferation of such measures poses the problem of evaluating their cost–effectiveness, which prompts the need for a comprehensive optimization methodology — capable of maximizing the resilience of the built environment. The aim of this paper is to lay out the foundations of a resilience–based framework for quantifying the performance of different infrastructure elements incurring blast threats, by means of functionality and resilience indicators. The proposed framework can quantify the consequences of multiple outdoor explosions typified by the emblematic car–bomb scenario. The level of localized damage is evaluated via pressure–impulse diagrams; local failures are then aggregated into the definition of resilience and functionality indicators, designed to provide the analyst with a comprehensive picture of global damage, residual functionality, and downtime of the structural system.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".