Enhancing Self-Compacting Concrete Performance by Substituting Fine Limestone with Wood Ash
Bibliographic record
Abstract
This work falls within the scope of waste valorization and reuse, specifically agricultural and vegetal waste, in the formulation of self-compacting concrete (SCC) which mainly consist of limestone fines.The production of these fines requires energy consumption and negatively impacts the environment.Partial replacement of these fines with wood ash, which is generated in large quantities from forest incineration, combustion of vegetal waste, and charcoal preparation, becomes a solution to reduce the production cost of these fines and preserve the environment.The study involved creating reference SCC with no wood ash, then replacing the fine limestone with wood ash in volumes of 6.12%, 18%, and 24%, and analyzing the properties of the resulting concrete in both fresh and hardened states.Tests included density, slump flow, L-Box, segregation resistance, Vfunnel, air content, compressive strength, flexural tensile strength, sclerometer resistance, ultrasonic pulse velocity, and elastic modulus, as well as analysis by DRX and FTIR.The findings indicate that partial replacement of fine limestone with wood ash improves the compactness and stability of the concrete, and reduces spreading, particularly in a confined environment.Additionally, the study shows that the SCC with wood ash substitution meets the European standard EFNARC and exhibits acceptable mechanical performance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".