Influence of Date Palm Waste Aggregates on the Mechanical Strengths and Hygroscopicity Behavior of Earth-Based Composites
Bibliographic record
Abstract
The underutilization of natural waste from date palm plantation maintenance presents an opportunity for the production of sustainable building materials.This study investigates the mechanical properties and environmental sustainability of adobe bricks reinforced with date palm waste (DPW) and a small percentage of cement.Adobe bricks were stabilized using 7% cement by weight and varying proportions of DPW (0%, 0.5%, 1%, and 1.5% by weight), followed by curing under two distinct conditions: moist storage (MS) and open-air (AF).It was observed that bricks cured under MS conditions significantly outperformed those cured in AF, evidenced by a 47.05% reduction in capillary absorption coefficient compared to the reference brick.Despite a decrease in compressive strength due to DPW incorporation, the bricks exhibited increases in capillary and total absorption while still satisfying earth construction standards.Notably, flexural strength improved by 41.66% under MS curing.Enhanced erosion and abrasion resistance, as well as improved performance throughout wetting/drying cycles, were also recorded.These enhancements underscore the potential of DPW as a renewable additive in the formulation of adobe bricks for ecological and durable housing.The study not only proposes a novel use for date palm byproducts but also contributes to the advancement of environmentally-friendly construction methodologies.
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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".