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Record W2611348901 · doi:10.4236/njgc.2017.72004

Effect of Glass Powder on Concrete Sustainability

2017· article· en· W2611348901 on OpenAlexaff
Ablam Zidol, Monique Tohoué Tognonvi, Arezki Tagnit‐Hamou

Bibliographic record

VenueNew Journal of Glass and Ceramics · 2017
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsGround granulated blast-furnace slagDurabilityMaterials scienceCementitiousPozzolanFly ashCompressive strengthChloridePermeability (electromagnetism)Composite materialProperties of concreteCementMetallurgyPortland cementChemistry

Abstract

fetched live from OpenAlex

As defined by the American Concrete Institute (ACI), alternative supplementary cementitious materials (ASCMs) and local materials are very important in concrete sustainability. As an ASCM, glass powder (GP) shows excellent pozzolanic properties. This paper focuses on characterization and the effect of GP on concrete properties compared to those of Class F fly ash (FFA) and ground granulated blast furnace slag (GGBS). Concrete incorporating 0, 20 and 30% of GP and other concrete mixes containing 30% of FFA or GGBS were cast. The concrete mixes considered in this study have water to binder (w/b) mass ratio ranging from 0.35 to 0.65. The mechanical properties such as compressive strength and durability including chloride ions permeability and chloride ions diffusion are evaluated. The results show that GP develops effects on mechanical properties similar to those of FFA and performs better than GGBS and FFA in terms of permeability reduction. GP reduces dramatically chloride permeability of concrete regardless w/b ratio, favoring an improvement of the concrete durability. Because of the interesting permeability developed by concretes incorporating GP, its use as an ASCM is promising.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.577
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.009
GPT teacher head0.276
Teacher spread0.267 · 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.

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

Citations63
Published2017
Admission routes1
Has abstractyes

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