Enhanced Thermo-Physical Properties of Gypsum Composites Using Olive Pomace Waste Reinforcement
Bibliographic record
Abstract
Today, the construction sector consumes 30-40% of the world's total energy and contributes one-third of total greenhouse gas emissions.Consequently, the development of new eco-friendly building materials with improved properties is becoming increasingly important.Olive pomace waste is released into the environment, which has a negative impact on it.Recycling this olive pomace waste as an alternative raw material in the construction industry can protect the environment and at the same time reduce the additional costs of managing and disposing of this waste for local authorities.It is an environmentally friendly and sustainable solution to waste recycling.This study investigated the effect of adding olive pomace (OP) to building materials.Four proportions of this additive (4%, 8%, 12% and 16%) were used.Physical, thermal properties (conductivity and diffusivity) as well as mechanical properties (compressive and flexural strength) of the composites were carried out.The traditional gypsum-based composites had a thermal conductivity of 0.478 W.m -1 .K -1 , while the composites of gypsum with additive show an interesting thermal conductivity of 0.390 W.m -1 .K -1 for a percentage of 16% (OP16) with a reduction rate of 22.56%, and mechanical properties lower than those of the reference gypsum-based composite but in accordance with the standard EN 133279-1, with compressive strength of almost 4.10MPa for a percentage of 16% (OP16), and flexural strength equal to 2MPa.This is due to the increase in porosity as indicated by the microstructure of the composites.We also tested water absorption by capillary action for each specimen, and found that this coefficient increased with increasing percentage of waste.
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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 | 0.001 | 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".