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Optimization of Drilling Cuttings Reactivity as a Supplementary Cementitious Material in Ternary Cements

2023· article· en· W4388568699 on OpenAlexaff
Kuelson Rândello Dantas Maciel, Ana Rita Damasceno Costa, Josinorma Silva de Oliveira, Heloysa Martins Carvalho Andrade, Jardel Pereira Gonçalves

Bibliographic record

VenueMaterials Research · 2023
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsDiscovery Air (Canada)
FundersFundação de Amparo à Pesquisa do Estado da BahiaConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsPortland cementMaterials scienceCementitiousCompressive strengthCementComposite materialTernary operation

Abstract

fetched live from OpenAlex

This study evaluates the influence of milling on the reactivity of drilling cuttings (DC) utilized as supplementary cementitious material in ternary cements (TC). The drilling cuttings milling study varied the time (2, 5, 10, 15, and 20 min) and rotation speed (200 and 300 rpm), determining the specific milling energy and grindability index. The hydration of TC pastes containing DC with different particle size distributions was evaluated by isothermal calorimetry during the first 72 hours, XRD/Rietveld at 3 and 28 days, compressive strength and absorption. The incorporation of milled DC improved the TC hydration kinetics compared to reference pastes of ordinary Portland cement (REF.PC). After 28 days, the TC pastes with the D50% diameter smaller than 11 µm reached at least 70% of the resistance to the strength of the Portland cement paste. Milled DC contributes to the physical and nucleation effect of the TC pastes studied and can be used as an SCM.

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)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.045
GPT teacher head0.339
Teacher spread0.294 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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