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Performance of High Strength Cementitious Composites with High Volume Supplementary Cementitious Materials

2020· article· en· W3111233021 on OpenAlexaff
Adeyemi Adesina, Emad Booya, Karla Gorospe, Sreekanta Das

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsCementitiousMaterials sciencePortland cementSilica fumeCompressive strengthComposite materialCementRaw materialSlag (welding)Volume (thermodynamics)

Abstract

fetched live from OpenAlex

Abstract Portland cement (PC) is the major binder used for cementitious composites. However, due to the increase in the demand for cementitious composites for the construction of infrastructures, there’s a consequential effect of the production of this binder (i.e. PC) on the sustainability of our environment. The production of PC emits huge amounts of carbon dioxide into the environment and posses a huge strain on the natural deposits of its raw material. In order to create a sustainable environment while meeting the high demand for cementitious composites; it is paramount to replace the PC with locally available waste materials. This study incorporates a high volume of slag alongside silica fume at a ratio of 2.2 to that of PC to produce high strength cementitious composites. The effects of these compositions were determined experimentally on its fresh and hardened properties. Results from this study showed that high strength cementitious composites can be produced with a high volume of supplementary cementitious composites up to 80%. The use of slag as 80% replacement of slag resulted in a 9.3% increase in the compressive strength and a 48.1% decrease in sorption.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.002
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.193
Teacher spread0.183 · 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.

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

Citations0
Published2020
Admission routes1
Has abstractyes

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