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Record W2059864993 · doi:10.3141/2164-03

Grid Types Used to Strengthen Reinforced Concrete Panels Subjected to Impact Loading

2010· article· en· W2059864993 on OpenAlexaff
Muslim Majeed, A O Abd El Halim, O. Burkan Isgor, E. Contestable

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2010
Typearticle
Languageen
FieldEngineering
TopicTransportation Safety and Impact Analysis
Canadian institutionsCarleton University
Fundersnot available
KeywordsRetrofittingExplosive materialStructural engineeringMaterials scienceReinforced concreteFailure mode and effects analysisPenetration (warfare)Forensic engineeringEngineering

Abstract

fetched live from OpenAlex

The protection of existing reinforced concrete structures against impact loads, the effects of blast, or both, has become a major concern of researchers and engineers. Sudden impacts and release of high-speed loads, such as those from bullets, fragment bombs, and bare explosive charges, can generate high pressures on structures, resulting in fragmentation that can cause serious damage, injuries, and casualties. Results from an experimental investigation performed on reinforced concrete slabs retrofitted with different grids and polypropylene, polyethylene, and steel meshes are presented. The testing program includes impact tests using a falling weight and fragmentation simulation using an air gun. These tests were conducted on square concrete panels with dimensions of 600 × 600 × 100 mm. Deflections and depths of penetration were measured, as were applied and absorbed kinetic energy and the kinetic energy of the resulting concrete fragments. Damage and mode of failure due to post-impact were also observed and recorded. Results and analysis of the data and observations showed that the concrete panels retrofitted with a combination of steel mesh and polyethylene grid provided the most promising retrofitting protection when compared with other options.

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 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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.806
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.069
GPT teacher head0.363
Teacher spread0.294 · 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 teacher head, not a consensus.

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

Citations0
Published2010
Admission routes1
Has abstractyes

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