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Simplified Framework for Blast-Risk-Based Cost-Benefit Analysis for Reinforced Concrete-Block Buildings

2015· article· en· W1883090976 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Performance of Constructed Facilities · 2015
Typearticle
Languageen
FieldEngineering
TopicStructural Response to Dynamic Loads
Canadian institutionsMcMaster University
FundersFederal Emergency Management AgencyNational Institute of Standards and TechnologyU.S. Department of Defense
KeywordsProbabilistic logicShear wallLimit state designFragilityProbabilistic risk assessmentDowntimeRisk managementEngineeringReliability engineeringComputer scienceBlock (permutation group theory)Structural engineeringRisk assessmentRisk analysis (engineering)Mathematics

Abstract

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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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.246
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it