Performance of Coconut Shell as Coarse Aggregate in Concrete
Bibliographic record
Abstract
A large amount of waste coconut shell is generated in India from temples and industries of coconut product and its disposal need to be addressed. Researchers have proposed to utilize it as an ingredient of concrete. This experimental investigation aimed to quantify the effects of replacing partially the conventional coarse aggregate with coconut shells to produce concrete. It was found that with an increasing proportion of coconut shells, there is a decrement in compressive strength. In our experimental study, we replaced coarse aggregate with coconut shell by 10%, 20%,30%, and 40%. Results revealed that with 10%,20%,30%, and 40% replacement of conventional coarse aggregate by coconut shells, the decrease in 28 days compressive strength is 15.4%,35.7%,46.1%, and 61.5% respectively. For 10%,20%30%, and 40% replacement of coconut shells, the decrease in 28 days tensile strength is 9%,18%,27.5%, and 36.5% respectively. For 10%,20%30%, and 40% replacement of coarse aggregate by coconut shells, the decrease in 28 days flexural strength is 22.7%,45.47%,68%, and 90.86% respectively. It is visualized that coconut shell replacement up to 20% with coarse aggregate exhibit better strength. The advantages of replacing conventional coarse aggregate with coconut shells include efficient utilization of waste coconut shells, reduction in natural source depletion, production of lightweight concrete, etc, the use of coconut shells in concrete seems to be a feasible option. Such a study will help to arrive at a final decision regarding the number of coconut shells for replacing conventional aggregates in concrete production.
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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".