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Ground Glass Pozzolan in Conventional, High, and Ultra-High Performance Concrete

2018· article· en· W4238176997 on OpenAlexaff
Arezki Tagnit‐Hamou, Ablam Zidol, Nancy Soliman, Joris Deschamps, Ahmed Omran

Bibliographic record

VenueMATEC Web of Conferences · 2018
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsFinenessPozzolanMaterials scienceCementitiousCementComposite materialGrindingGlass recyclingFly ashPortland cement

Abstract

fetched live from OpenAlex

Ground-glass pozzolan (G) obtained by grinding the mixed-waste glass to same fineness of cement can act as a supplementary-cementitious material (SCM), given that it is an amorphous and a pozzolanic material. The G showed promising performances in different concrete types such as conventional concrete (CC), high-performance concrete (HPC), and ultra-high performance concrete (UHPC). The current paper reports on the characteristics and performance of G in these concrete types. The use of G provides several advantages (technological, economical, and environmental). It reduces the production cost of concrete and decrease the carbon footprint of a traditional concrete structures. The rheology of fresh concrete can be improved due to the replacement of cement by non-absorptive glass particles. Strength and rigidity improvements in the concrete containing G are due to the fact that glass particles act as inclusions having a very high strength and elastic modulus that have a strengthening effect on the overall hardened matrix.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.018
GPT teacher head0.240
Teacher spread0.222 · 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 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

Citations8
Published2018
Admission routes1
Has abstractyes

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