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Record W1512120207 · doi:10.5539/jmsr.v4n4p19

Freezing Thawing Resistance of Concrete Incorporating Glass Waste

2015· article· en· W1512120207 on OpenAlexvenueno aff
Raed M. Abendeh, Khaled Ramadan, Rami H. Haddad, Mousa BaniBaker

Bibliographic record

VenueJournal of Materials Science Research · 2015
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceDurabilityAggregate (composite)Flexural strengthComposite materialCompressive strengthProperties of concreteCementYoung's modulusElastic modulus

Abstract

fetched live from OpenAlex

Glass waste, which is dumped around the world in huge amounts, can be used as a partial replacement of mineral aggregate in concrete industry. This would not only contribute to reducing pollution extent but also enhancing physical properties and durability of concrete. This article reports the mechanical performance of concrete with different replacement percentages of crushed glass waste of cement or fine aggregates before and after being subjected to standard cycles of freezing and thawing. Mechanical performance was evaluated in terms of compressive and flexural strengths. Furthermore, internal damage extent was evaluated using ultrasonic pulse velocity and dynamic modulus of elasticity. The results (average of six specimens for each test) revealed the feasibility of incorporating glass waste in concrete mixtures for the purpose of improving strength and durability; especially in environments where concrete is being exposed to an effective freezing and thawing cycles.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 head0.091
GPT teacher head0.351
Teacher spread0.260 · 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 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

Citations7
Published2015
Admission routes1
Has abstractyes

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