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Record W4297394962 · doi:10.53063/synsint.2022.23120

Effect of glass waste as a cement replacement on the mechanical properties of concrete

2022· article· en· W4297394962 on OpenAlexvenueno aff
Sara Ahmadi

Bibliographic record

VenueSynthesis and Sintering · 2022
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
Fundersnot available
KeywordsCementCompressive strengthDispose patternFlexural strengthMaterials scienceGlass recyclingComposite materialEnvironmental pollutionProperties of concreteWaste managementEnvironmental science

Abstract

fetched live from OpenAlex

Every day several tons of glass dispose as waste. Glass waste, as a non-degradable waste, causes many environmental problems. Using glass waste powder in concrete as a partial substitute for cement has notable effects on the reduction of environmental pollutants, energy consumption, and concrete production costs. In this study, the impact of using waste glass powder in levels of 5, 10, 15, and 20 wt.% as a substitute for cement on the mechanical properties of concrete was evaluated. Chemical analysis of glass and cement samples was determined using X-ray fluorescence (XRF). The flexural and compressive strength of the samples were measured according to ISO 679, Methods of testing cement – Determination of strength, in 3, 7, 28, and 90 days. The results of the compressive strength test showed that the strength of concrete increases by the amount of used glass powder in the concrete composition. The highest value of compressive strength was obtained by the sample with 15 wt.% of glass powder.

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 head0.017
GPT teacher head0.228
Teacher spread0.211 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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