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Record W3111198015 · doi:10.1680/jmacr.20.00170

Development of low-carbon masonry grout mixtures using alkali-activated binder

2020· article· en· W3111198015 on OpenAlexaff
Adeyemi Adesina, Sreekanta Das

Bibliographic record

VenueMagazine of Concrete Research · 2020
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsGroutMasonryPortland cementCementMaterials scienceCompressive strengthLimeSlag (welding)Composite materialMetallurgyStructural engineeringEngineering

Abstract

fetched live from OpenAlex

This paper presents the results of an experimental evaluation of masonry grout made with lime-activated slag–glass powder blend as a binder. The purpose of this study is to develop a grout mixture that is low-carbon dioxide (‘low-carbon’) and has the potential to replace the traditional grout material used in load-bearing concrete block masonry construction. Traditional grout material uses Portland cement, production of which is responsible for a large proportion of carbon dioxide emissions. Thus, elimination or even reduction in the use of Portland cement will minimise environmental impacts. Three grout mixtures incorporating different proportions of glass powder as a partial replacement of slag as an aluminosilicate precursor were investigated to determine the optimum mix proportion that yields the desired properties of the grout material used in concrete block masonry construction. The workability, compressive strength and permeability properties of the mixtures were evaluated. Results from this study show that the use of glass powder at 25% replacement of slag as a precursor in the binder system provides the best performance, and that this mixture can be successfully used as an alternative and low-carbon grout material for concrete block masonry construction.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.868

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.082
GPT teacher head0.320
Teacher spread0.238 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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