The Impact of Incorporating Shell Aggregates and Coconut Fibres on the Physical, Thermal and Mechanical Properties of Cement Mortars
Bibliographic record
Abstract
The reuse of shell residues in building materials offers an effective strategy for waste recovery and reducing environmental impact.In this work, shell waste is examined as a partial substitute for natural sand in cement mortar formulations.In the parallel, natural coconut fibres were used to study their effect on the mixture incorporating the shell aggregates.The particle size distribution has been adjusted to resemble that of fine sand by removing ultrafine fractions smaller than 0.08 mm.We replaced the natural sand with three different percentages of shell sand (15%, 25% and 45% by mass) and three different percentages of coconut fibre (0%, 8% and 16% by volume).An evaluation of selected properties of the material was carried out, including its mechanical, thermal and capillary behaviour.The results show that replacement with shell sand and coconut fibre improves the mortar's thermal properties.The incorporation of shell waste in cement mortar (CM) to substitute 45% of the sand is anticipated to increase the thermal insulation efficiency of the material of 21.70% and 23.57% for, CM450 and CM4516 respectively.This replacement has a negative effect on mechanical strength, with the reduction in compressive strength ranging from 20.44% for CM450 to 31.49% for CM4516.However, replacing 8% of the coconut fibres gives flexural strength values close to those of the reference mortar.In terms of capillarity, shell aggregates have a tendency to reduce the capillarity coefficient of mortars reaching a rate of 58.33% for CM450, but the fibres react in the opposite way.
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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".