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Record W4399787839 · doi:10.4028/p-gw4vsr

A Review on the Effect of Marble Powder on Properties of Self-Compacting Concrete

2024· review· en· W4399787839 on OpenAlexaff
Mohamed Amin, Marijana Hadzima-Nyarko, Ibrahim Saad Agwa, Abdullah M. Zeyad, Bassam A. Tayeh, Adeyemi Adesina, Afonso Rangel Garcez de Azevedo

Bibliographic record

VenueAdvances in science and technology · 2024
Typereview
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsMaterials scienceUltimate tensile strengthCompressive strengthFlexural strengthAggregate (composite)CementGrindingProperties of concreteComposite materialMetallurgy

Abstract

fetched live from OpenAlex

Marble powder (MP) emerges as a byproduct during the cutting and grinding operations of marble stone, constituting a non-biodegradable fine powder. This residue, originating from the marble stone industry, holds the potential for sustainable development when incorporated into self-compacting concrete (SCC). A thorough examination of existing literature underscores the substantial promise of MP as either a supplement or substitute for both cement and fine aggregate in concrete compositions. The literature review provides a comprehensive overview of the incorporation of MP in SCC. An evident trend in the reviewed studies indicates that as the proportion of MP used instead of fine aggregate increases, the fresh properties of the concrete tend to diminish. Nevertheless, the chemical composition of marble, containing CaCO3and SiO2, contributes positively to the mechanical properties of the concrete. Notably, when MP is employed as a replacement for fine aggregate at ratios ranging from 15% to 75%, a discernible enhancement in mechanical properties, ranging from 10% to 30%, is observed. Conversely, substituting MP for cement in quantities exceeding 20% exhibits detrimental effects on both the fresh and mechanical properties of the concrete. The impact of MP on various facets of SCC, including workability, setting times, compressive strength (CS), splitting tensile strength (STS), and flexural strength (FS) has been thoroughly investigated and discussed. This scrutiny contributes valuable insights into the potential advantages and challenges associated with the incorporation of MP in SCC.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.025
GPT teacher head0.333
Teacher spread0.309 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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