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Record W2115983051 · doi:10.1139/l09-032

Fundamental concepts in blast resistance evaluation of structuresThis article is one of a selection of papers published in the Special Issue on Blast Engineering.

2009· article· en· W2115983051 on OpenAlexaffvenue
A. Ghani Razaqpur, Waleed Mekky, S. Foo

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicStructural Response to Dynamic Loads
Canadian institutionsMcMaster UniversityPublic Works and Government Services Canada
Fundersnot available
KeywordsDeflection (physics)Flexural strengthImpulse (physics)Structural engineeringBlast waveEngineeringComputer sciencePhysicsAerospace engineering

Abstract

fetched live from OpenAlex

This study critically discusses the fundamental concepts used for evaluating the flexural and axial resistance of structures under blast. Simplified methods based on single degree of freedom are emphasized. The paper begins with how to estimate the blast parameters for a given charge size and standoff distance. These parameters include side-on and reflected pressures, positive phase duration, and side-on and reflected impulses. Subsequently, blast damage criteria are defined in accordance with prevailing guidelines and some of their short comings are discussed. To assess the impact of blast on the flexural safety and performance of structures, some simple methods are presented. The methods are either empirical or are based on the principles of energy and momentum conservation. The analytical results are in closed-form or in the form of pressure–impulse (P–I) diagrams. The effect of strain rate on both blast-induced flexural deflection and strength of structures, with particular emphasis on reinforced concrete structures, is discussed.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.003
Scholarly communication0.0020.004
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.007
GPT teacher head0.216
Teacher spread0.209 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2009
Admission routes2
Has abstractyes

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