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Record W4225808244 · doi:10.24018/ejeng.2021.6.7.2642

Properties of Concrete Made from Cupola Furnace Slag and Recycled Construction Aggregates

2021· article· en· W4225808244 on OpenAlexaff
A. J. Adese, O. D. Olajide, O. A. Olaoyenikan

Bibliographic record

VenueEuropean Journal of Engineering and Technology Research · 2021
Typearticle
Languageen
FieldEngineering
TopicRecycled Aggregate Concrete Performance
Canadian institutionsConcordia University
Fundersnot available
KeywordsCupolaCompressive strengthPortland cementMaterials scienceSlag (welding)CementMetallurgyGround granulated blast-furnace slagPorosityComposite material

Abstract

fetched live from OpenAlex

The replacement of traditional constituents of concrete with waste has garnered a lot of interest due to the increasing demand for natural resources used in concrete production. Hence, this study presents the outcome of an experimental investigation on the effect of partial replacement of Ordinary Portland Cement (OPC) at percentage levels of 0% to 25% in steps of 5%, partial and complete replacement of sand and granite in the percentage of 0 to 50 in steps of 10% with cupola slag and recycled construction aggregates. The fresh and hardened properties of concrete such as workability, compressive strength, and microstructural characteristics were assessed. The test results showed a decrease in workability as the percentage replacement of cupola furnace slag (CFS) and recycled construction aggregates (RCA) increases. Concrete produced at 28 days from the partial replacement of cement and sand with cupola slag has an optimum compressive strength of 20.37 N/mm2 at 10% and 22.81 N/mm2 at 20%, resulting in improved strength of 29.4% and 56.82%, respectively when compared with the control. In contrast, granite’s partial replacement with coarse cupola slag and complete replacement of sand and granite gave a lesser compressive strength when compared with the control samples at 7, 14, 28, and 56 days. The porosity of the concrete with optimum compressive strength from each mix was studied using microscopic and image analysis. The result showed that the addition of cupola slag in granulated and fine forms decreases the porosity, hence leading to higher compressive strength.

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)0.000
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.020
GPT teacher head0.224
Teacher spread0.204 · 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
Published2021
Admission routes1
Has abstractyes

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