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Record W2905321506 · doi:10.1680/jmacr.18.00271

Evaluation of the fresh and hardened state properties of low cement content systems

2018· article· en· W2905321506 on OpenAlexaff
Mayra T. de Grazia, Leandro Sanchez, Roberto Romano, Rafael Giuliano Pileggi

Bibliographic record

VenueMagazine of Concrete Research · 2018
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCementPortland cementCarbon footprintMaterials scienceClinker (cement)RheologyDurabilityCompressive strengthWater contentPorosityComposite materialYoung's modulusEnvironmental scienceWaste managementGeotechnical engineeringGreenhouse gasEngineeringGeology

Abstract

fetched live from OpenAlex

The production of Portland cement (PC) clinker is responsible for about 6·5% of total carbon dioxide (CO 2 ) emissions worldwide. Therefore, recent studies have focused on alternatives to decrease PC content and thus reduce the carbon footprint of concrete construction. Although guidelines suggest a minimum cement content of approximately 250–300 kg/m 3 depending on the type of the structure or structural member, there is currently a lack of information on the impact of the amount of cement on the overall behaviour of concrete. This work evaluates the influence of PC (ASTM C150 Type III) content on the fresh (i.e. rheological behaviour) and hardened (i.e. compressive strength, dynamic and static modulus of elasticity, porosity and permeability) properties of concrete mixtures produced with low to moderate (54, 159 and 260 kg/m 3 ) PC amounts. Results show that it is possible to produce eco-efficient concrete without compromising the fresh and hardened states of the material. However, the durability and long-term properties of low cement content systems should be further appraised.

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)0.001
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.148
GPT teacher head0.321
Teacher spread0.173 · 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

Citations20
Published2018
Admission routes1
Has abstractyes

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