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Record W4387772874 · doi:10.1617/s11527-023-02257-y

Standardisation of low clinker cements containing calcined clay and limestone: a review by RILEM TC-282 CCL

2023· review· en· W4387772874 on OpenAlexaff
Fragkoulis Kanavaris, Manuel Vieira, Shashank Bishnoi, Zengfeng Zhao, William Wilson, Arezki Tagnit Hamou, François Avet, Arnaud Castel, Franco Zunino, Visalakshi Talakokula, Fernando Martirena, Susan A. Bernal, Maria Juenger, Kyle A. Riding

Bibliographic record

VenueMaterials and Structures · 2023
Typereview
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversité de Sherbrooke
FundersEngineering and Physical Sciences Research Council
KeywordsCalcinationMaterials scienceSolid mechanicsClinker (cement)CementMineralogyComposite materialGeologyPortland cementChemistry

Abstract

fetched live from OpenAlex

Abstract Materials used in concrete construction are highly regulated through national standards that set minimum material reactivity, composition, and performance. Advances have shown that the combination of calcined clay and limestone fines in cementitious systems can have a synergistic reaction that allows for high levels of clinker replacement while maintaining adequate mechanical properties and durability. Recent modifications to national standards and codes have been made to allow for the use of calcined clay and limestone fines in concrete, albeit with some restrictions on use. Building codes also impose limits such as maximum water-to-cement/binder)-ratio, minimum strength, and minimum cement content as means to meet design service life requirements in lieu of measuring durability properties. This paper reviews the major standards and codes related to calcined clay materials and their use in concrete and suggests changes that could increase adoption and clinker replacement. It is hoped that this review will provide insights that can help facilitate the wider adoption of these materials in the construction industry as well as to identify potential changes in standards or creation of new ones which might be needed to enable the rapid widespread uptake of this promising technology.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.039
GPT teacher head0.344
Teacher spread0.305 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations22
Published2023
Admission routes1
Has abstractyes

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