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Record W2034198368 · doi:10.1520/jai12010

Mechanical Properties of ASR-Affected Concrete Containing Fine or Coarse Reactive Aggregates

2006· article· en· W2034198368 on OpenAlexaffabout
Nizar Smaoui, Benoı̂t Bissonnette, M-A Bérubé, Benoît Fournier, Bernard Durand

Bibliographic record

VenueJournal of ASTM International · 2006
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsHydro-QuébecNatural Resources CanadaCentre de Géomatique du QuébecUniversité Laval
Fundersnot available
KeywordsMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Abstract This experimental study was conducted to investigate the effects of the alkali-silica reactivity (ASR) on the mechanical properties of concrete, and in particular the strength and the modulus of elasticity under direct tension. Two highly reactive aggregates, a coarse (Québec City limestone) and a fine (Texas sand) were used. The results suggest that the effects of ASR on the mechanical properties of concrete may vary with the reactive aggregate involved. At least, they depend on the particle size and/or the degree of reactivity of this aggregate. The direct tensile strength was the mechanical property of concrete, the most affected and the most-rapidly affected by ASR. At 0.1 % expansion, for instance, concrete cylinders cast with the Québec City limestone showed moderate losses of 16 % in compressive strength and in splitting strength (Brazilian test), but as high as 48 % in direct tensile strength. The modulus of elasticity presented similar values in compression and in direct tension, and its reduction also gives a very good idea of the progress of the damage due to ASR. At 0.1 % expansion, for instance, concrete cylinders containing the Québec City limestone showed a loss in modulus of 19 % in direct tension and 23 % in compression.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.785

Codex and Gemma teacher scores by category

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

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 head0.021
GPT teacher head0.253
Teacher spread0.233 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations38
Published2006
Admission routes2
Has abstractyes

Explore more

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