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
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it