A Review on the Effect of Marble Powder on Properties of Self-Compacting Concrete
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".