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Record W2047147043 · doi:10.1680/macr.13.00096

Critical overview of blast resistance of different concrete types

2013· article· en· W2047147043 on OpenAlexaff
Monir M. A. Alhadid, Ahmed M. Soliman, Moncef L. Nehdi, Maged A. Youssef

Bibliographic record

VenueMagazine of Concrete Research · 2013
Typearticle
Languageen
FieldEngineering
TopicStructural Response to Dynamic Loads
Canadian institutionsWestern University
Fundersnot available
KeywordsRetrofittingStructural engineeringEngineeringImpulse (physics)Blast waveForensic engineering

Abstract

fetched live from OpenAlex

Past attacks against buildings and civil infrastructure highlight the need for blast-resistant structural materials. Numerous studies have been conducted on various related techniques and some design guidance has been developed to increase the resistance of structures to blast loading. Generally, a blast results in a high-amplitude impulse loading typically with a very short duration. Hence, the material's response to such loading will differ from its response to regular types of loading. Consequently, the analysis and design of structures subjected to blast loads require a full understanding of the behaviour of materials and structural elements under blast loading. This paper presents an overview of the behaviour of concrete elements subjected to blast loads. A critical discussion of the state of the art on blast resistance of conventional and modern concrete materials is provided, along with an overview of effective retrofitting and strengthening techniques.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.002

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 head0.042
GPT teacher head0.337
Teacher spread0.295 · 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 designObservational
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

Citations22
Published2013
Admission routes1
Has abstractyes

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