Simplified Framework for Blast-Risk-Based Cost-Benefit Analysis for Reinforced Concrete-Block Buildings
Bibliographic record
Abstract
Probabilistic risk assessment (PRA) is essential for evaluating different options for blast risk management. However, depending on the risk management approach being considered, a rigorous blast PRA study can be quite demanding. To expedite this process, a simplified PRA framework is proposed for reinforced concrete-block shear wall buildings, in order to determine design basis threat (DBT) fragility curves based on revised damage limit states most suitable for risk assessment. The current definitions of damage states by North American standards for blast resistant design involve global response limits—such as the support rotations of a structural element—that are relatively simple to calculate. However, such damage state descriptors can be insufficient for the cost-benefit analysis required to evaluate different risk mitigation options. As such, building on recent advances in the area of performance-based seismic design of concrete-block shear wall buildings, this study proposes revised damage states that can be associated with more useful metrics, including repair technique and building downtime. To illustrate the proposed methodology, a hypothetical shear wall building is analyzed under different DBT levels. The DBT fragility curves are obtained through Monte Carlo sampling of the random variables describing the shear wall system and are used to identify the locations that are most suitable for the erection of barriers for blast protection. The proposed PRA framework can be used to identify target performance requirements, formulated in terms of stakeholders’ tolerable probability of failure and consequent risk management, for different classes of buildings under a range of DBTs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".