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Record W3041591662 · doi:10.21838/uhpc.2016.35

Green Ultra-High-Performance Glass Concrete

2016· article· en· W3041591662 on OpenAlexaffabout
Arezki Tagnit‐Hamou, Nancy Soliman, Ahmed Omran

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsRheologyMaterials scienceFlexural strengthComposite materialSlumpQuartzFinenessCuring (chemistry)Silica fumeCompressive strengthCementMechanical strength

Abstract

fetched live from OpenAlex

This paper presents research work on the development of a green type of ultra-high-performance concrete at the University of Sherbrooke using ground glass powders with different degrees of fineness (UHPGC). In UHPGC, glass is used to replace quartz sand, cement, quartz powder, and silica-fume particles. UHPGC design is based on particle packing density, mechanical properties, and specific rheology. Mixes are designed to suit the rheology and mechanical performances of different concrete applications. Depending on composition and curing conditions, UHPGC can provide improved rheology (mini-slump spread of 260 mm), higher mechanical properties (compressive strength greater than 200 MPa, flexural strength more than 25 MPa). A case study of using this UHPGC is presented through the design and construction of a footbridge.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.084
Threshold uncertainty score1.000

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)0.0020.001

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 head0.010
GPT teacher head0.199
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; both teacher heads agree on what is shown here.

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

Citations52
Published2016
Admission routes2
Has abstractyes

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