A Cameroonian Study on Mixing Concrete with Wood Ashes: Effects of 0-30% Wood Ashes as a Substitute of Cement on the Strength of Concretes
Bibliographic record
Abstract
To fight against the high cost and the increasing scarcity of cement and at the same time to reduce the CO2 greenhouse gases emission associated with the production of Portland cement, two types of wood ashes as a substitute of cement in the production of concretes were investigated. In this paper, we substituted cement by two types of species of wood ashes namely, avocado and eucalyptus ashes following the proportions ranging from 0% to 30% on one hand, and on the other hand, we added these two types of species of wood ashes namely, avocado and eucalyptus ashes following the proportions ranging from 0% to 10% by weight of cement in the concrete samples. After 7, 14 and 28 days of curing, compressive strength tests were conducted on these concrete samples. The findings revealed that using wood ashes as additives/admixtures or as a substitute of cement in the production/manufacturing of concrete decreased the compressive strength of concrete. Hence, it can be said that wood ash has a negative influence on the strength of concrete. At three percent (3%) and ten percent (10%) of addition, the wood ash from eucalyptus specie offers better resistance compared to the wood ash from avocado specie, whereas at five percent (5%) of addition, the wood ash from avocado specie offers better resistance compared to the wood ash from eucalyptus specie. At thirty percent (30%) of substitution, the wood ash from eucalyptus specie offers better resistance compared to the wood ash from avocado specie. The compressive strengths increase with the increase of curing age.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".