Resilience of Masonry Systems in Nuclear Power Plants Under Blast Risk
Bibliographic record
Abstract
The resilience of the built environment to high explosives poses a significant challenge to the professionals tasked with the design of blast resistant facilities. Current standards — including the ASCE 59-11 and CSA S850-12 — fail to address this challenge in design provisions targeting a single parameter of structural performance, while neglecting other key indicators of performance recovery that define the very concept of resilience. In order to investigate their significance in the design process, two resilience parameters known as robustness and rapidity are evaluated for an archetype blast scenario — a nuclear power plant (NPP) featuring reinforced concrete block masonry walls exposed to a blast hazard, namely, the detonation of an explosive charge within an open (outdoor) area of the industrial complex. The adopted methodology integrates resilience–based analysis and probabilistic risk assessment, in order to account for the uncertainties associated with threat (attack likelihood); hazard (attacker’s success likelihood); load input variables — including location, mass, and type of explosive; resistance variables — including material properties and wall geometry; and loss variables — including the costs of repair and replacement. Based on the current analysis, recommendations are made to incorporate resilience metrics in standards for blast protection, so as to foster more resilient industrial facilities.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".