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Record W2018068524 · doi:10.1177/0021998311401089

Durability performance of fiber-reinforced concrete in severe environments

2011· article· en· W2018068524 on OpenAlexaff
B. Kim, Andrew J. Boyd, J.-Y. Lee

Bibliographic record

VenueJournal of Composite Materials · 2011
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaterials scienceDurabilityComposite materialCarbonationFiber-reinforced concreteScanning electron microscopePolypropyleneFiberToughnessPolyvinyl alcohol

Abstract

fetched live from OpenAlex

Polypropylene (PP; 0.5%), polyvinyl alcohol (PVA; 0.75%), and hooked-end steel (1%) fibers were investigated to evaluate the durability performance of fiber-reinforced concrete (FRC) exposed to severe environments. Conventional beam specimens (100 × 100 × 360 mm 3 ) were prepared and exposed to three types of conditioning systems for 27 months, in both un-cracked and pre-cracked conditions. Degradation of the FRC was evaluated using visual or photographic inspection, change in permeable pore space, destructive beam testing, scanning electron microscopy analysis, and depth of carbonation measurements. For each of the fiber types and mixtures evaluated, significant surface degradation and carbonation only appeared in specimens exposed to immersion in a low pH solution designed to simulate swamp water. These specimens also exhibited significant degradation in both average residual strength (ARS) and toughness. On the other hand, difficulties in the comparison between pre-cracked specimens and un-cracked specimens were found due to re-adhered or healed pre-cracked specimens from dissolved materials (salt or lime) in solutions. A relatively good resistance to saltwater immersion and w/d conditioning was observed for all fiber types. Among fiber types, steel fibers showed the highest strength to conditioning compared with PP and PVA fibers.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.012
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.000

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 head0.014
GPT teacher head0.203
Teacher spread0.189 · 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 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

Citations27
Published2011
Admission routes1
Has abstractyes

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